Basic Data Mining with n-Grams
Sin Ying WOng
10/30/2019
Load Package
library(readtext)
library(tidyverse)
library(stringr)
library(tm)
library(RWeka)
library(SnowballC)
library(wordcloud)Read Data
data <-readtext('C:/Users/wsyin/Documents/Data 607/Presentation/Text_NLP.txt')
text <- data$text
text <- text %>%
str_remove_all('[^[:alnum:][:space:]]') %>%
str_replace_all('\\n', ' ')
#textCreate functions for n-grams and tidying data
BigramTokenizer <- function(x) NGramTokenizer(x, Weka_control(min = 2, max = 2))
TrigramTokenizer <- function(x) NGramTokenizer(x, Weka_control(min = 3, max = 3))
FourgramTokenizer <- function(x) NGramTokenizer(x, Weka_control(min = 4, max = 4))
FivegramTokenizer <- function(x,n) NGramTokenizer(x, Weka_control(min = 5, max = 5))
removeURL <- function(x) str_replace_all(x,"http[[:alnum:]]*", "")Create a VCorpus for data, perform tidying
text_corpus <- VCorpus(VectorSource(text)) %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(removeWords, stopwords("en")) %>%
tm_map(content_transformer(tolower)) %>%
tm_map(content_transformer(removeURL)) %>%
tm_map(stripWhitespace)Create a character class to store high frequency n_grams
key_n_gram <- character()Create term-document matrix on tri-grams, observe high frequency and meaningful tri-grams, insert into key_n_gram
tdm.trigram <- TermDocumentMatrix(text_corpus,
control = list(wordLengths = c(10,Inf),
tokenize = TrigramTokenizer))
#inspect(tdm.bigram)
freq.tdm.trigram <- data.frame(word = tdm.trigram$dimnames$Terms, frequency = tdm.trigram$v, stringsAsFactors = FALSE) %>%
arrange(-frequency)
freq.tdm.trigramObserve high frequency and meaningful tri-grams, insert into key_n_gram
key_n_gram <- c(key_n_gram, freq.tdm.trigram$word[1])
key_n_gram## [1] "natural language processing"Create term-document matrix on bi-grams
tdm.bigram <- TermDocumentMatrix(text_corpus,
control = list(wordLengths = c(8,Inf),
tokenize = BigramTokenizer))
#inspect(tdm.bigram)
freq.tdm.bigram <- data.frame(word = tdm.bigram$dimnames$Terms, frequency = tdm.bigram$v, stringsAsFactors = FALSE) %>%
arrange(-frequency)
freq.tdm.bigramInsert high frequency and meaningful bi-grams into key_n_grams
key_n_gram <- c(key_n_gram,freq.tdm.bigram$word[1:10])
key_n_gram## [1] "natural language processing" "natural language"
## [3] "machine learning" "neural networks"
## [5] "language processing" "language model"
## [7] "nlp machine" "learning nlp"
## [9] "computational linguistics" "parse tree"
## [11] "lookup table"Optional: create term-document matrix on four-gram, observed no meaningful patterns, and so will for five-gram
tdm.4gram <- TermDocumentMatrix(text_corpus,
control = list(wordLengths = c(14,Inf),
tokenize = FourgramTokenizer))
#inspect(tdm.bigram)
freq.tdm.4gram <- data.frame(word = tdm.4gram$dimnames$Terms, frequency = tdm.4gram$v) %>%
arrange(-frequency)
freq.tdm.4gramConcatenating n-grams by ’_’
text_corpus_mod <- text_corpus
for (key in key_n_gram){
text_corpus_mod <- text_corpus_mod[[1]]$content %>%
str_replace_all(key,str_replace_all(key,' ','_')) %>%
VectorSource() %>%
VCorpus()
}
text_corpus_mod## <<VCorpus>>
## Metadata: corpus specific: 0, document level (indexed): 0
## Content: documents: 1dataframe<-data.frame(text=unlist(sapply(text_corpus_mod, `[`, "content")), stringsAsFactors=F)
dataframe$text[1]## [1] " proceedings nd annual meeting association computational_linguistics system demonstrations pages â baltimore maryland usa june c association computational_linguistics the stanford corenlp natural_language_processing toolkit christopher d manning linguistics computer science stanford university manningstanfordedu mihai surdeanu sista university arizona msurdeanuemailarizonaedu john bauer dept computer science stanford university horatiostanfordedu jenny finkel prismatic inc jrfinkelgmailcom steven j bethard computer information sciences u alabama birmingham bethardcisuabedu david mcclosky ibm research dmccloskyusibmcom abstract we describe design use stanford corenlp toolkit extensible pipeline provides core natural_language analysis this toolkit quite widely used research nlp community also among commercial government users open source nlp technology we suggest follows simple approachable design straightforward interfaces inclusion robust good quality analysis components requiring use large amount associated baggage introduction this paper describe design development stanford corenlp java least jvmbased annotation pipeline framework provides common core natural_language_processing nlp steps tokenization coreference resolution we describe original design system strengths section simple usage patterns section set provided annotators properties control section add additional annotators section concluding higherlevel remarks additional appendices while several good natural_language analysis toolkits stanford corenlp one used central theme trying identify attributes contributed success original design development our pipeline system initially designed internal use previously combining multiple natural_language analysis components ad hoc apis tied together custom glue code the initial version tokenizaon sentencespling partofspeechtagging morphologicalanalysis namedentyrecognion syntaccparsing otherannotators coreferenceresoluon raw text execuonflow annotaon object annotated text tokenize ssplit pos lemma ner parse dcoref gender sentiment figure overall system architecture raw text put annotation object sequence annotators add information analysis pipeline the resulting annotation containing analysis information added annotators can output xml plain text forms annotation pipeline developed order replace jumble something better a uniform interface provided annotator adds kind analysis information text an annotator taking annotation object can add extra information an annotation stored typesafe heterogeneous map following ideas data type presented bloch this basic architecture proven quite successful still basis system described it illustrated figure the motivations â to able quickly painlessly get linguistic annotations text â to hide variations across components behind common api â to minimal conceptual footprint system easy learn â to provide lightweight framework using plain java objects rather something heavier weight xml uimaâs common analysis system cas objects in initially part multisite grant project system extended easily usable broader range users we provided commandline interface ability write annotation various formats including xml further work led system released free open source software on one hand architectural perspective stanford corenlp attempt everything it nothing straightforward pipeline architecture it provides java api it attempt provide multiple machine scaleout though provide multithreaded processing single machine it provides simple concrete api but requirements satisfy large percentage potential users resulting simplicity makes easier users get started framework that primary advantage stanford corenlp larger frameworks like uima ferrucci lally gate cunningham et al users learn uima gate can get started need know little java in practice large important differentiator if complex scenarios required multiple machine scaleout can normally achieved running analysis pipeline within system focuses distributed workflows hadoop spark other systems attempt provide uiuc curator clarke et al includes intermachine clientserver communication processing caching natural_language analyses but functionality comes cost the system complex install complex understand moreover practice organization may well committed scaleout solution different provided natural_language analysis toolkit for example may using kryo googleâs protobuf binary serialization rather apache thrift underlies curator in case user better served fairly small selfcontained natural_language analysis system rather something comes lot baggage sorts purposes using on hand users benefit greatly provision set stable robust high nevertheless can call analysis component written languages via appropriate wrapper annotator turn wrapped many people provide stanford corenlp bindings languages quality linguistic analysis components can easily invoked common scenarios while builder larger system may made overall design choices handle scaleout unlikely nlp expert hence looking nlp components just work this huge advantage stanford corenlp gate empty toolbox apache uima download something addressed part development wellintegrated component packages uima cleartk bethard et al dkpro core gurevych et al jcore hahn et al however solution provided packages remains harder learn complex heavier weight users pipeline described these attributes echo patricio argued made hibernate successful including one thing well ii avoid overdesign iii running ten minutes less indeed design success stanford corenlp also reflects several factors patricio highlights including iv avoid standardism v documentation vi developer responsiveness while many factors contribute uptake project hard show causality believe attributes account fact stanford corenlp one used nlp toolkits while certainly done perfect job compared much academic software stanford corenlp gained attributes clear open source licensing modicum attention documentation attempting answer user questions elementary usage a key design goal make simple set run processing pipelines either api commandline using api running pipeline can easy figure or commandline linguistic processing file can easy figure real life rarely simple ability get started using product minimal configuration code gives new users good initial experience figure gives realistic complete example use showing several key properties system an annotation pipeline can applied text paragraph whole story rather just single sentence the behavior annotator pipeline new stanfordcorenlp annotation annotation new annotation can parse sentence pipelineannotateannotation figure minimal code analysis pipeline export stanfordcorenlphome whereinstalled java xmxg cp stanfordcorenlphome edustanfordnlpstanfordcorenlp file inputtxt figure minimal commandline invocation import javaio import javautil import edustanfordnlpio import edustanfordnlpling import edustanfordnlppipeline import edustanfordnlptrees import edustanfordnlptreestreecoreannotations import edustanfordnlputil public class stanfordcorenlpexample public static void mainstring args throws ioexception printwriter xmlout new printwriterxmloutputxml properties props new properties propssetpropertyannotators tokenize ssplit pos lemma ner parse stanfordcorenlp pipeline new stanfordcorenlpprops annotation annotation new annotation this short sentence and another pipelineannotateannotation pipelinexmlprintannotation xmlout an annotation map can get use various analyses individually for instance gets parse_tree st sentence text list sentences annotationget coreannotationssentencesannotationclass sentences null sentencessize coremap sentence sentencesget tree tree sentencegettreeannotationclass printwriter new printwritersystemout outprintlnthe first sentence parsed treepennprintout figure a simple complete example program annotators pipeline controlled standard java properties properties object the basic property specify annotators run order shown but discussed annotators properties allow customization usage if none specified reasonable defaults used running pipeline simple first example show two possibilities accessing results first convert annotation object xml write file second show code gets particular type information annotation prints our presentation shows usage java stanford corenlp pipeline wrapped others can accessed easily many languages including python ruby perl scala clojure javascript nodejs net languages including c f provided annotators the annotators provided stanfordcorenlp can work character encoding making use javaâs good unicode support system defaults utf encoding the annotators also support processing various human languages providing suitable underlying models resources available different languages the system comes packaged models english separate model packages provide support chinese caseinsensitive processing english support languages less complete many annotators also support models french german arabic see appendix b building models languages possible using underlying tools in section outline provided annotators focusing english versions it noted models underlying annotators trained annotated corpora using supervised machine_learning others rulebased components nevertheless often require language resources tokenize tokenizes text sequence tokens the english component provides ptbstyle tokenizer extended reasonably handle noisy web text the corresponding components chinese arabic provide word clitic segmentation the tokenizer saves character offsets token input text cleanxml removes xml tags document ssplit splits sequence tokens sentences truecase determines likely true case tokens text likely case welledited text information lost eg upper case text this implemented discriminative model using crf sequence tagger finkel et al pos labels tokens partofspeech pos tag using maximum entropy pos tagger toutanova et al lemma generates lemmas base forms tokens annotation gender adds likely gender information names ner recognizes named person location organization misc numerical money number date time duration set entities with default annotators named entities recognized using combination crf sequence taggers trained various corpora finkel et al numerical entities recognized using two rulebased systems one money numbers separate stateoftheart system processing temporal expressions chang manning regexner implements simple rulebased ner token sequences building java regular expressions the goal annotator provide simple framework allow user incorporate ne labels annotated traditional nl corpora for example default list regular expressions distribute models file recognizes ideologies ideology nationalities nationality religions religion titles title parse provides full syntactic analysis including constituent dependency representation based probabilistic parser klein manning de marneffe et al sentiment sentiment analysis compositional model trees using deep learning socher et al nodes binarized tree sentence including particular root node sentence given sentiment score dcoref implements mention detection pronominal nominal coreference resolution lee et al the entire coreference graph text head words mentions nodes provided annotation most annotators various options can controlled properties these can either added properties object creating annotation pipeline via api specified either commandline flags properties file running system commandline as simple example input system may already tokenized presented onesentenceperline in case wish tokenization sentence splitting just work using whitespace rather trying anything creative right wrong this can accomplished adding two properties either properties file tokenizewhitespace true sspliteolonly true code simple annotator locations stored gazetteer package orgfoo public class gazetteerlocationannotator implements annotator method annotator must implement public void annotateannotation annotation traverse sentences document coremap sentenceannotationgetsentencesannotationclass loop tokens sentence text already tokenized list toks sentencegettokensannotationclass int start start tokssize start assumes gazetteer returns token index match otherwise int end gazetteerislocationtoks start end start int start end toksgetisetnamedentitytagannotationclasslocation figure an example simple custom annotator the annotator marks words possibly multiword locations gazetteer propssetpropertytokenizewhitespace true propssetpropertysspliteolonly true via commandline flags tokenizewhitespace sspliteolonly we attempt describe properties understood annotator available documentation stanford corenlp however note follow pattern xy x name annotator apply adding annotators while users work provided annotators quite easy add additional custom annotators system we illustrate write annotator code load stanford corenlp system an annotator class implements three methods single method analysis two describe dependencies analysis steps public void annotateannotation annotation public set requirementssatisfied public set requires the information annotation updated place usually nondestructive manner adding new keys values annotation the code simple annotator marks locations contained gazetteer shown figure similar code can used write wrapper annotator calls preexisting analysis component adds results annotation the functionality annotator already provided regexner annotator serves simple example while building analysis pipeline stanford corenlp can add additional annotators pipeline loaded using reflection to provide new annotator user extends class edustanfordnlppipelineannotator provides constructor signature string properties then user adds property customannotatorclassfoo bar properties used create pipeline if foo added list annotators class bar will loaded instantiate the properties object also passed constructor annotatorspecific behavior can initialized properties object for instance example properties file lines might customannotatorclasslocgaz orgfoogazetteerlocationannotator annotators tokenizessplitlocgaz locgazmaxlength conclusion in paper presented design usage stanford corenlp system annotationbased nlp processing pipeline we particular tried emphasize properties feel made successful rather trying provide largest engineered kitchen sink goal make easy possible users get started using framework keep framework small easily comprehensible can easily used component within much larger system user may developing the broad usage system systems nltk bird et al emphasize accessibility beginning users suggests merits approach a pointers website corenlpshtml github maven stanfordnlpstanfordcorenlp license gpl v stanford corenlp keeps models machine_learning components miscellaneous data files separate models jar file if using maven need make sure list dependency models file well code jar file you can code like following pomxml note extra dependency classifier element bottom edustanfordnlp stanfordcorenlp edustanfordnlp stanfordcorenlp models b human language support we summarize analysis components supported different human languages early annotator ara chi eng fre gerbic nese lish nch man tokenize x x x x x sent split x x x x x truecase x pos x x x x x lemma x gender x ner x x x regexner x x x x x parse x x x x x dep parse x x sentiment x coref x c getting sentiment sentences we show commandline sentiment analysis cat sentimenttxt i liked it fantastic experience the plot move rather slowly java cp xmxg edustanfordnlppipelinestanfordcorenlp annotators tokenizessplitposlemmaparsesentiment file sentimenttxt adding annotator tokenize adding annotator ssplit adding annotator pos reading pos tagger model edustanfordnlpmodelspostagger englishleftwordsenglishleftwordsdistsimtagger done sec adding annotator lemma adding annotator parse loading parser serialized file edustanfordnlpmodelslexparser englishpcfgsergz done sec adding annotator sentiment ready process files skipped total processing file usersmanningsoftwarestanfordcorenlpfull sentimenttxt writing usersmanningsoftware stanfordcorenlpfullsentimenttxtxml annotating file usersmanningsoftwarestanfordcorenlpfull sentimenttxt seconds seconds processed documents skipped documents error annotating documents annotation pipeline timing information ptbtokenizerannotator sec wordstosentencesannotator sec postaggerannotator sec morphaannotator sec parserannotator sec sentimentannotator sec total sec tokens tokenssec pipeline setup sec total time stanfordcorenlp pipeline sec grep sentiment sentimenttxtxml d use within uima the main part using stanford corenlp within uima framework ferrucci lally mapping corenlp annotations regular java classes uima annotations declared via xml type descriptors uimaspecific java classes generated a wrapper corenlp will typically define subclass jcasannotator implbase whose process method extracts uima annotations cas ii converts uima annotations corenlp annotations iii runs corenlp input annotations iv converts corenlp output annotations uima annotations v saves uima annotations cas to illustrate part process cleartk bethard et al wrapper converts corenlp token annotations uima annotations saves cas following code int begin tokenanngetcharacteroffsetbeginannotationclass int end tokenanngetcharacteroffsetendannotationclass string pos tokenanngetpartofspeechannotationclass string lemma tokenanngetlemmaannotationclass token token new tokenjcas begin end tokensetpospos tokensetlemmalemma tokenaddtoindexes token uima type declared token orgcleartktokentypetoken uimatcasannotation pos uimacasstring lemma uimacasstring references steven bethard philip ogren lee becker cleartk design patterns machine_learning uima in lrec steven bird ewan klein edward loper natural_language_processing python oâreilly media joshua bloch effective java addison wesley upper saddle river nj nd edition angel x chang christopher d manning sutime a library recognizing normalizing time expressions in lrec james clarke vivek srikumar mark sammons dan roth an nlp curator how i learned stop worrying love nlp pipelines in lrec hamish cunningham diana maynard kalina bontcheva valentin tablan gate architecture development robust hlt applications in acl mariecatherine de marneffe bill maccartney christopher d manning generating typed dependency parses phrase structure parses in lrec pages â david ferrucci adam lally uima architectural approach unstructured information processing corporate research environment natural_language engineering â jenny rose finkel trond grenager christopher manning incorporating nonlocal information information extraction systems gibbs sampling in acl pages â i gurevych m muhlh â auser c m â uller j steimle â m weimer t zesch darmstadt knowledge processing repository based uima in first workshop unstructured information management architecture gldv tubingen â u hahn e buyko r landefeld m muhlhausen â poprat m k tomanek j wermter an overview jcore julie lab uima component registry in lrec dan klein christopher d manning fast exact inference factored model natural_language parsing in suzanna becker sebastian thrun klaus obermayer editors advances neural information processing systems volume pages â mit press heeyoung lee angel chang yves peirsman nathanael chambers mihai surdeanu dan jurafsky deterministic coreference resolution based entitycentric precisionranked rules computational_linguistics anthony patricio why project successful whythisprojectissuccessful richard socher alex perelygin jean wu jason chuang christopher d manning andrew ng christopher potts recursive deep models semantic compositionality sentiment treebank in emnlp pages â kristina toutanova dan klein christopher d manning yoram singer featurerich partofspeech tagging cyclic dependency network in naacl pages â journal machine_learning research submitted revised published natural_language_processing almost scratch ronan collobertâ\210 ronancollobertcom jason westonâ jwestongooglecom leon bottou â â leonbottouorg michael karlen michaelkarlengmailcom koray kavukcuogluâ koraycsnyuedu pavel kuksaâ pkuksacsrutgersedu nec laboratories america independence way princeton nj editor michael collins abstract we propose unified neural network architecture learning algorithm can applied various natural_language_processing tasks including partofspeech tagging chunking named entity recognition semantic role labeling this versatility achieved trying avoid taskspecific engineering therefore disregarding lot prior knowledge instead exploiting manmade input features carefully optimized task system learns internal representations basis vast amounts mostly unlabeled training data this work used basis building freely available tagging system good performance minimal computational requirements keywords natural_language_processing neural_networks introduction will computer program ever able convert piece english text programmer friendly data structure describes meaning natural_language text unfortunately consensus emerged form existence data structure until fundamental articial intelligence problems resolved computer scientists must settle reduced objective extracting simpler representations describe limited aspects textual information these simpler representations often motivated specific applications instance bagofwords variants information retrieval belief capture something general natural_language they can describe syntactic information eg partofspeech tagging chunking parsing semantic information eg wordsense disambiguation semantic role labeling named entity extraction anaphora resolution text corpora manually annotated data structures order compare performance various systems the availability standard benchmarks stimulated research natural_language_processing nlp â\210 ronan collobert now idiap research institute switzerland â jason weston now google new york ny â leon bottou now microsoft redmond wa â â koray kavukcuoglu also new york university new york ny â pavel kuksa also rutgers university new brunswick nj c ronan collobert jason weston leon bottou michael karlen koray kavukcuoglu pavel kuk â sa collobert weston bottou karlen kavukcuoglu and kuksa effective systems designed tasks such systems often viewed software components constructing realworld nlp solutions the overwhelming majority stateoftheart systems address single benchmark task applying linear statistical models adhoc features in words researchers discover intermediate representations engineering taskspecific features these features often derived output preexisting systems leading complex runtime dependencies this approach effective researchers leverage large body linguistic knowledge on hand great temptation optimize performance system specific benchmark although performance improvements can useful practice teach us little means progress toward broader goals natural_language understanding elusive goals artificial intelligence in contribution try excel multiple benchmarks avoiding taskspecific engineering instead use single learning system able discover adequate internal representations in fact view benchmarks indirect measurements relevance internal representations discovered learning procedure posit intermediate representations general benchmarks our desire avoid taskspecific engineered features prevented us using large body linguistic knowledge instead reach good performance levels tasks transferring intermediate representations discovered large unlabeled data sets we call approach âœalmost scratchâ emphasize reduced still important reliance priori nlp knowledge the paper organized follows section describes benchmark tasks interest section describes unified model reports benchmark results obtained supervised training section leverages large unlabeled data sets â\210 million words train model language_modeling task performance improvements demonstrated transferring unsupervised internal representations supervised benchmark models section investigates multitask supervised training section evaluates much improvement can achieved incorporating standard nlp taskspecific engineering systems drifting away initial goals gives us opportunity construct allpurpose tagger simultaneously accurate practical fast we conclude short discussion section the benchmark tasks in section briefly introduce four standard nlp tasks will benchmark architectures within paper partofspeech tagging pos chunking chunk named entity recognition ner semantic role labeling srl for consider standard experimental setup give overview stateoftheart systems setup the experimental setups summarized table stateoftheart systems reported table partofspeech tagging pos aims labeling word unique tag indicates syntactic role example plural noun adverb a standard benchmark setup described detail toutanova et al sections â wall street journal wsj data used training sections â validation sections â testing the best pos classifiers based classifiers trained windows text fed bidirectional decoding algorithm inference features include preceding following natural_language_processing almost from scratch task benchmark data set training set test set tokens tokens tags pos toutanova et al wsj sections â sections â chunking conll wsj sections â section iobes ner conll reuters âœengtrainâ âœengtestbâ iobes srl conll wsj sections â section brown sections iobes table experimental setup task report standard benchmark used data set relates well training test information system accuracy shen et al toutanova et al gimenez m â arquez pos system f shen sarkar sha pereira kudo matsumoto b chunk system f ando zhang florian et al kudo matsumoto c ner system f koomen et al pradhan et al haghighi et al d srl table stateoftheart systems four nlp tasks performance reported perword accuracy pos f score chunk ner srl systems bold will referred benchmark systems rest paper see section tag context well multiple words bigrams trigrams context handcrafted features deal unknown words toutanova et al use maximum entropy classifiers inference bidirectional dependency network heckerman et al reach perword accuracy gimenez m â arquez proposed svm approach also trained text windows bidirectional inference achieved two viterbi decoders lefttoright righttoleft they obtained perword accuracy more recently shen et al pushed stateoftheart new learning algorithm call guided learning also bidirectional sequence classification collobert weston bottou karlen kavukcuoglu and kuksa chunking also called shallow parsing chunking aims labeling segments sentence syntactic constituents noun verb phrases np vp each word assigned one unique tag often encoded beginchunk eg bnp insidechunk tag eg inp chunking often evaluated using conll shared task sections â wsj data used training section testing validation achieved splitting training set kudoh matsumoto won conll challenge chunking fscore their system based support vector machines svms each svm trained pairwise classification manner fed window around word interest containing pos words features well surrounding tags they perform dynamic programming test time later improved results kudo matsumoto using ensemble classifiers trained different tagging conventions see section since certain number systems based secondorder random fields reported sha pereira mcdonald et al sun et al reporting around f score these systems use features composed words pos tags tags more recently shen sarkar obtained using voting classifier scheme classifier trained different tag representations iob ioe they use pos features coming external tagger well carefully handcrafted specialization features change data representation concatenating carefully chosen chunk tags words pos representation they build trigrams features finally passed viterbi decoder test time named entity recognition ner labels atomic elements sentence categories âœpersonâ âœlocationâ as chunking task word assigned tag prefixed indicator beginning inside entity the conll setup ner benchmark data set based reuters data the contest provides training validation testing sets florian et al presented best system ner conll challenge f score they used combination various machinelearning classifiers features picked included words pos tags chunk tags prefixes suffixes large gazetteer provided challenge well output two ner classifiers trained richer data sets chieu second best performer conll f also used external gazetteer performance goes gazetteer several handchosen features later ando zhang reached f semisupervised approach they trained jointly linear model ner linear model two auxiliary unsupervised tasks they also performed viterbi decoding test time the unlabeled corpus m words taken reuters features included words pos tags suffixes prefixes chunk tags overall less specialized conll challengers see see table tagging scheme details see natural_language_processing almost from scratch semantic role labeling srl aims giving semantic role syntactic constituent sentence in propbank palmer et al formalism one assigns roles arg words arguments verb technically predicate sentence example following sentence might tagged âœjohnarg aterel applearg â âœateâ predicate the precise arguments depend verbâs frame multiple verbs sentence words might multiple tags in addition arg tags several modifier tags argmloc locational argmtmp temporal operate similar way verbs we picked conll srl benchmark it takes sections â wsj data training set section validation set a test set composed section wsj concatenated sections brown corpus also provided challenge stateoftheart srl systems consist several stages producing parse_tree identifying parse_tree nodes represent arguments given verb finally classifying nodes compute corresponding srl tags this entails extracting numerous base features parse_tree feeding statistical models feature categories commonly used system include gildea jurafsky pradhan et al â parts speech syntactic labels words nodes tree â nodeâs position left right relation verb â syntactic path verb parse_tree â whether node parse_tree part noun verb phrase â voice sentence active passive â nodeâs head word â verb subcategorization pradhan et al take base features define additional features notably partofspeech tag head word predicted named entity class argument features providing word sense disambiguation verb add variants new feature types overall this system close stateoftheart performance pradhan et al obtain f system based svm classifiers simultaneously using two parse_trees provided srl task in spirit haghighi et al use loglinear models tree node reranked globally dynamic algorithm their system reaches using five top charniak parse_trees koomen et al hold stateoftheart winnowlike littlestone classifiers followed decoding stage based integer program enforces specific constraints srl tags they reach f conll thanks five top parse_trees produced charniak parser first one provided contest well collins parse_tree see collobert weston bottou karlen kavukcuoglu and kuksa evaluation in experiments strictly followed standard evaluation procedure conll challenges ner chunk srl in particular chose hyperparameters model according simple validation procedure see remark later section performed validation set available task see section all three tasks evaluated computing f scores chunks produced models the pos task evaluated computing perword accuracy case standard benchmark refer toutanova et al we used conlleval script evaluating pos ner chunk for srl used srlevalpl script included srlconll package discussion when participating open challenge legitimate increase generalization means it thus surprising see many top conll systems using external labeled data like additional ner classifiers ner architecture florian et al additional parse_trees srl systems koomen et al combining multiple systems tweaking carefully features also common approach like chunking top system shen sarkar however comparing systems learn anything quality system trained different labeled data for reason will refer benchmark systems top existing systems avoid usage external data wellestablished nlp field toutanova et al pos sha pereira chunking for ner consider ando zhang using additional unlabeled data we picked koomen et al srl keeping mind use additional parse_trees provided challenge these benchmark systems will serve baseline references experiments we marked bold table we note four tasks considering work can seen complex tasks corresponding lower accuracies best systems proposed engineered features relative best systems simpler tasks that pos task one simplest four tasks relatively engineered features whereas srl complex many kinds features designed this clearly implications yet unsolved nlp tasks requiring sophisticated semantic understanding ones considered the networks all nlp tasks can seen tasks assigning labels words the traditional nlp approach extract sentence rich set handdesigned features fed standard classification algorithm example support vector machine svm often linear kernel the choice features completely empirical process mainly based first linguistic intuition trial error feature selection task dependent implying additional research new nlp task complex tasks like srl require large number possibly available we used âœrâ option conlleval script get perword accuracy pos available natural_language_processing almost from scratch input window lookup_table linear hardtanh linear text cat sat mat feature w w w n feature k w k w k w k n xxxxxxxxxxlt w xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxltw k xxxxm ã â m ã â word interest d concat xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx n hu xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx n hu tags figure window approach network complex features eg extracted parse_tree can impact computational cost might important largescale applications applications requiring realtime response instead advocate radically different approach input will try preprocess features little possible use multilayer neural network nn architecture trained endtoend fashion the architecture takes input sentence learns several layers feature extraction process inputs the features computed deep layers network automatically trained backpropagation relevant task we describe section general multilayer architecture suitable nlp tasks generalizable nlp tasks well our architecture summarized figure figure the first layer extracts features word the second layer extracts features window words whole sentence treating sequence local global structure ie treated like bag words the following layers standard nn layers notations we consider neural network fîâ parameters î any feedforward neural network l layers can seen composition functions f l î â corresponding layer l fîâ f l î f lâ\210 î f î â collobert weston bottou karlen kavukcuoglu and kuksa input sentence lookup_table convolution max over time linear hardtanh linear text the cat sat mat feature w w w n feature k w k w k w k n xxxxxxxxxxxxxxxxxxxxlt lt w xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxw k xxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx maxâ m ã â m ã â d padding padding n hu m ã â xxxxxxxxxxxxxxxxxxxx n hu xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx n hu xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxx n hu tags figure sentence approach network in following will describe layer use networks shown figure figure we adopt notations given matrix a denote a j coefficient row column j matrix we also denote hai dwin vector obtained concatenating dwin column vectors around th column vector matrix a â\210\210 r dãd h hai dwin it a iâ\210dwin a diâ\210dwin a idwin a didwin natural_language_processing almost from scratch as special case hai represents th column matrix a for vector v denote v scalar index vector finally sequence element x x xt written x t the th element sequence x transforming words feature vectors one key points architecture ability perform well use almost raw words the ability method learn good word representations thus crucial approach for efficiency words fed architecture indices taken finite dictionary d obviously simple index carry much useful information word however first layer network maps word indices feature vector lookup_table operation given task interest relevant representation word given corresponding lookup_table feature vector trained backpropagation starting random initialization we will see section can learn good word representations unlabeled corpora our architecture allow us take advantage better trained word representations simply initializing word lookup_table representations instead randomly more formally word w â\210\210 d internal dwrddimensional feature vector representation given lookup_table layer ltw â ltw w hwi w w â\210\210 r dwrdãd matrix parameters learned hwi w â\210\210 r dwrd w th column w dwrd word vector size hyperparameter chosen user given sentence sequence t words w t d lookup_table layer applies operation word sequence producing following output matrix ltw w t hwi w hwi w hwi wt this matrix can fed neural network layers will see extending to any discrete features one might want provide features words one suspects features helpful task interest for example ner task one provide feature says word gazetteer another common practice introduce basic preprocessing wordstemming dealing upper lower case in latter option word represented three discrete features lower case stemmed root lower case ending capitalization feature generally speaking can consider word represented k discrete features w â\210\210 d ã âââ ãdk dk dictionary k th feature we associate feature lookup_table ltwk â parameters wk â\210\210 r d k wrdãdk d k wrd â\210\210 n userspecified vector size given we preprocessing namely lowercasing encoding capitalization another feature with enough unlabeled training data presumably learn model without processing ideally even raw input learn letter sequences rather words however felt beyond scope work as neural network layer collobert weston bottou karlen kavukcuoglu and kuksa word w feature vector dimension dwrd â\210k d k wrd obtained concatenating lookup_table outputs ltw wk w ï ïï ltw w ltwk wk ï ïï ï ïï hw w hwki wk ï ïï the matrix output lookup_table layer sequence words w t similar extra rows added discrete feature ltw wk w t ï ïï hw w hw wt hwki wk hwki wkt ï ïï these vector features lookup_table effectively learn features words dictionary now want use trainable features input layers trainable feature extractors can represent groups words finally sentences extracting higher level features word feature vectors feature vectors produced lookup_table layer need combined subsequent layers neural network produce tag decision word sentence producing tags element variable length sequences sentence sequence words standard problem machinelearning we consider two common approaches tag one word time window approach convolutional sentence approach window approach a window approach assumes tag word depends mainly neighboring words given word tag consider fixed size ksz hyperparameter window words around word each word window first passed lookup_table layer producing matrix word features fixed size dwrd ãksz this matrix can viewed dwrd kszdimensional vector concatenating column vector can fed neural network layers more formally word feature window given first network layer can written f î hltw w t dwin t ï ïïïïïïïï hwi w tâ\210dwin hwi w t hwi w tdwin ï ïïïïïïïï linear layer the fixed size vector f î can fed one several standard neural network layers perform affine transformations inputs f l î wl f lâ\210 î b l wl â\210\210 r n l huãn lâ\210 hu b l â\210\210 r n l hu parameters trained the hyperparameter n l hu usually called number hidden units l th layer natural_language_processing almost from scratch hardtanh layer several linear layers often stacked interleaved nonlinearity function extract highly nonlinear features if nonlinearity introduced network simple linear model we chose âœhardâ version hyperbolic tangent nonlinearity it advantage slightly cheaper compute compared exact hyperbolic tangent leaving generalization performance unchanged collobert the corresponding layer l applies hardtanh input vector h f l î hardtanh h f lâ\210 î hardtanhx ï ï ï â\210 x â\210 x â\210 x x scoring finally output size last layer l network equal number possible tags task interest each output can interpreted score corresponding tag given input network thanks carefully chosen cost function will describe later section remark border effects the feature window well defined words near beginning end sentence to circumvent problem augment sentence special âœpaddingâ word replicated dwin times beginning end this akin use âœstartâ âœstopâ symbols sequence models sentence approach we will see experimental section window approach performs well natural_language_processing tasks interested however approach fails srl tag word depends verb correctly predicate chosen beforehand sentence if verb falls outside window one expect word tagged correctly in particular case tagging word requires consideration whole sentence when using neural_networks natural choice tackle problem becomes convolutional approach first introduced waibel et al also called time delay neural_networks tdnns literature we describe detail convolutional network it successively takes complete sentence passes lookup_table layer produces local features around word sentence thanks convolutional layers combines feature global feature vector can fed standard affine layers in semantic role labeling case operation performed word sentence verb sentence it thus necessary encode network architecture verb considering sentence word want tag for purpose word position sentence augmented two features way described section these features encode relative distances iâ\210 posv iâ\210 posw respect chosen verb position posv word tag position posw respectively convolutional layer a convolutional layer can seen generalization window approach given sequence represented columns matrix f lâ\210 î lookup_table matrix matrixvector operation applied window successive windows sequence collobert weston bottou karlen kavukcuoglu and kuksa xxthe proposed changes also allow executives report exercises options later less often xx xx xxxx xx xx xx xxxx xx xx xxxx x x x x x x x x x x x x x x x x x xx xx xx xx xx xx xx xx xx xx xx xxxx x x x x x x x x xx xx xxxx xx xxxxxxx xx xxthexxproposed changes also allow executives report exercises options later less often xx xx x x xx xx xx xx xx xx xx xx xx xxxx xx xx x x x x x x x x x x x xx xx xx xx xx xx xx xx xx x x x x xx xx xx xxxx xx xx xxxx xx xx xx xx xxxx xx xx xxxx xx figure number features chosen word position max layer we consider sentence approach network figure trained srl the number âœlocalâ features output convolution layer per word by applying max sentence obtain features whole sentence it interesting see network catches features mostly around verb interest âœreportâ word interest âœproposedâ left âœoftenâ right using previous notations t th output column l th layer can computed hf l î t wl hf lâ\210 î dwin t b l â\210t weight matrix wl across windows t sequence convolutional layers extract local features around window given sequence as standard affine layers convolutional layers often stacked extract higher level features in case layer must followed nonlinearity network equivalent one convolutional layer max layer the size output depends number words sentence fed network local feature vectors extracted convolutional layers combined obtain global feature vector fixed size independent sentence length order apply subsequent standard affine layers traditional convolutional networks often apply average possibly weighted max operation âœtimeâ t sequence here âœtimeâ just means position sentence term stems use convolutional layers example speech data sequence occurs time the average operation make much sense case general words sentence influence semantic role given word tag instead used max approach forces network capture useful local features produced convolutional layers see figure task hand given matrix f lâ\210 î output convolutional layer l â\210 max layer l outputs vector f l î h f l î max t h f lâ\210 î â â n lâ\210 hu this fixed sized global feature vector can fed standard affine network layers as window approach finally produce one score per possible tag given task remark the border effects arise convolution operation window approach we work around problem padding sentences special word natural_language_processing almost from scratch scheme begin inside end single other iob bx ix ix bx o ioe ix ix ex ex o iobes bx ix ex sx o table various tagging schemes each word segment labeled âœxâ tagged prefixed label depending word position segment begin inside end single word segment labeling also output words labeled segment labeled âœoâ variants iob ioe scheme exist prefix b e replaced i segments contiguous another segment label âœxâ tagging schemes as explained earlier network output layers compute scores possible tags task interest in window approach tags apply word located center window in convolutional sentence approach tags apply word designated additional markers network input the pos task indeed consists marking syntactic role word however remaining three tasks associate labels segments sentence this usually achieved using special tagging schemes identify segment boundaries shown table several schemes defined iob ioe iobes without clear conclusion scheme better general stateoftheart performance sometimes obtained combining classifiers trained different tagging schemes eg kudo matsumoto the ground truth ner chunk srl tasks provided using two different tagging schemes in order eliminate additional source variations decided use expressive iobes tagging scheme tasks for instance chunk task describe noun phrases using four different tags tag âœsnpâ used mark noun phrase containing single word otherwise tags âœbnpâ âœinpâ âœenpâ used mark first intermediate last words noun phrase an additional tag âœoâ marks words members chunk during testing tags converted original iob tagging scheme fed standard performance evaluation scripts mentioned section training all neural_networks trained maximizing likelihood training data using stochastic gradient ascent if denote î trainable parameters network trained using training set t want maximize following loglikelihood respect î î â â\210 x yâ\210\210t log py x î x corresponds either training word window sentence associated features y represents corresponding tag the probability pâ computed outputs neural network we will see section two ways interpreting neural network outputs probabilities collobert weston bottou karlen kavukcuoglu and kuksa wordlevel loglikelihood in approach word sentence considered independently given input example x network parameters î outputs score f î x th tag respect task interest to simplify notation drop x now write instead f î this score can interpreted conditional tag probability pi x î applying softmax bridle operation tags pi xî e f î â\210j e f î j defining logadd operation logadd zi logâ\210 e zi can express loglikelihood one training example x y follows log py x î fî y â\210logadd j fî j while training criterion often referred crossentropy widely used classification problems might ideal case often correlation tag word sentence neighboring tags we now describe another common approach neural_networks enforces dependencies predicted tags sentence sentencelevel loglikelihood in tasks like chunking ner srl know dependencies word tags sentence tags organized chunks tags follow tags training using wordlevel approach discards kind labeling information we consider training scheme takes account sentence structure given predictions tags network words sentence given score going one tag another tag want encourage valid paths tags training discouraging paths we consider matrix scores f î x t output network as drop input x t notation simplification the element f î matrix score output network parameters î sentence x t th tag t th word we introduce transition score a j jumping j tags successive words initial score a starting th tag as transition scores going trained network parameters î define îëœ îâ\210ª a j â\210i j the score sentence x t along path tags t given sum transition scores network scores sx t t îëœ t â\210 t a tâ\210 t fî t t exactly wordlevel likelihood normalizing respect tags using softmax normalize score possible tag paths j t using softmax interpret resulting ratio conditional tag path probability taking log conditional probability true path y t therefore given log py t x t îëœ sx t y t îëœâ\210logadd â\210 j t sx t j t îëœ natural_language_processing almost from scratch while number terms logadd operation equal number tags grows exponentially length sentence fortunately one can compute linear time following standard recursion t see rabiner taking advantage associativity distributivity semiring râ\210ª â\210â\210ž logadd îtk â\210 logadd j t â\210 j tk sx t j t îëœ logadd logadd j t â\210 j tâ\210iâ\210 j tk sx t j tâ\210 îëœ a j tâ\210 k fî kt logadd îtâ\210i a k fî kt fî kt logadd îtâ\210i a k â\210k followed termination logadd â\210 j t sx t j t îëœ logadd ît we can now maximize loglikelihood training pairs x t y t at inference time given sentence x t tag find best tag path minimizes sentence score in words must find argmax j t sx t j t îëœ the viterbi algorithm natural choice inference it corresponds performing recursion logadd replaced max tracking back optimal path max remark graph transformer networks our approach particular case discriminative forward training graph transformer networks gtns bottou et al le cun et al the loglikelihood can viewed difference forward score constrained valid paths case labeled path unconstrained forward score remark conditional random fields an important feature equation absence normalization summing exponentials e f î possible tags necessarily yield unity if case scores viewed logarithms conditional transition probabilities model subject labelbias problem motivates conditional random fields crfs lafferty et al the denormalized scores instead likened potential functions crf in fact crf maximizes likelihood using linear model instead nonlinear neural network crfs widely used nlp world pos tagging lafferty et al chunking sha pereira ner mccallum li srl cohn blunsom compared crfs take advantage nonlinear network learn appropriate features task interest in words read logadd âš âš collobert weston bottou karlen kavukcuoglu and kuksa stochastic gradient maximizing stochastic gradient bottou achieved iteratively selecting random example x y making gradient step î ââ\210 îî â\210log py x î â\210î î chosen learning rate our neural_networks described figure figure succession layers correspond successive composition functions the neural network finally composed wordlevel loglikelihood successively composed recursion using sentencelevel loglikelihood thus analytical formulation derivative can computed applying differentiation chain rule network wordlevel loglikelihood recurrence remark differentiability our cost functions differentiable almost everywhere nondifferentiable points arise use âœhardâ transfer function use âœmaxâ layer sentence approach network fortunately stochastic gradient still converges meaningful local minimum despite minor differentiability problems bottou stochastic gradient iterations hit nondifferentiability simply skipped remark modular approach the well known âœbackpropagationâ algorithm lecun rumelhart et al computes gradients using chain rule the chain rule can also used modular implementation our modules correspond boxes figure figure given derivatives respect outputs module can independently compute derivatives respect inputs respect trainable parameters proposed bottou gallinari this allows us easily build variants networks for details gradient computations see appendix a remark tricks many tricks reported training neural_networks lecun et al which ones choose often confusing we employed two initialization update parameters network layer done according âœfaninâ layer number inputs used compute output layer plaut hinton the fanin lookup_table lth linear layer convolution layer respectively nlâ\210 hu dwin ãn lâ\210 hu the initial parameters network drawn centered uniform distribution variance equal inverse squareroot fanin the learning rate divided fanin stays fixed training supervised benchmark results for pos chunking ner tasks report results window architecture described section the srl task trained using sentence approach section results reported table perword accuracy pwa pos f score tasks we performed experiments wordlevel loglikelihood wll sentencelevel loglikelihood sll the hyperparameters networks reported table all see we found training tasks complex sentence approach computationally expensive offered little performance benefits results discussed section provide insight decision natural_language_processing almost from scratch approach pos chunking ner srl pwa f f f benchmark systems nnwll nnsll table comparison generalization performance benchmark nlp systems vanilla neural network nn approach pos chunking ner srl tasks we report results wordlevel loglikelihood wll sentencelevel loglikelihood sll generalization performance reported perword accuracy rate pwa pos f score tasks the nn results behind benchmark results section show improve models using unlabeled data task windowconv size word dim caps dim hidden units learning rate pos dwin d d n hu î chunk â â â â â ner â â â â â srl â â â n hu n hu â table hyperparameters networks they chosen minimal validation see remark preferring identical parameters tasks we report task window size convolution size word feature dimension capital feature dimension number hidden units learning rate networks fed two raw text features lower case words capital letter feature we chose consider lower case words limit number words dictionary however keep upper case information lost transformation added âœcapsâ feature tells word lowercase uppercase first letter capital least one noninitial capital letter additionally occurrences sequences numbers within word replaced string âœnumberâ example words âœpsâ âœpsâ map single word âœpsnumberâ we used dictionary containing common words wsj case insensitive words outside dictionary replaced single special âœrareâ word results show neural_networks âœoutoftheboxâ behind baseline benchmark systems although initial performance networks falls short performance conll challenge winners compares honorably performance competitors the training criterion takes account sentence structure sll seems boost performance chunking ner srl tasks little advantage pos this result line existing nlp studies comparing sentencelevel wordlevel likelihoods liang et al the capacity network architectures lies mainly word lookup_table contains ã parameters train in wsj data common words appear time many words appear times it thus difficult train properly corresponding collobert weston bottou karlen kavukcuoglu and kuksa france jesus xbox reddish scratched megabits persuade thickets decadent widescreen odd ppa faw savary divo antica anchieta uddin blackstock sympathetic verus shabby emigration biologically giorgi jfk oxide awe marking kayak shaheed khwarazm urbina thud heuer mclarens rumelia stationery epos occupant sambhaji gladwin planum ilias eglinton revised worshippers centrally goaâuld gsnumber edging leavened ritsuko indonesia collation operator frg pandionidae lifeless moneo bacha wj namsos shirt mahan nilgiris table word embeddings word lookup_table srl neural network trained scratch dictionary size for column queried word followed index dictionary higher means rare nearest neighbors arbitrarily using euclidean metric dimensional feature vectors lookup_table ideally like semantically similar words close embedding space represented word lookup_table continuity neural network function tags produced semantically similar sentences similar we show table case neighboring words embedding space seem semantically related we will focus next section improving word embeddings leveraging unlabeled data we will see approach results performance boost tasks remark architectures in experiments paper tuned hyperparameters trying different architectures validation in practice choice hyperparameters number hidden units provided large enough limited impact generalization performance in figure report f score task validation set respect number hidden units considering variance related network initialization chose smallest network achieving âœreasonableâ performance rather picking network achieving top performance obtained single run remark training time training network quite computationally expensive chunking ner take one hour train pos takes hours srl takes three days training faster larger learning rate preferred stick small one works rather finding optimal one speed second order methods lecun et al another speedup technique lots unlabeled data we like obtain word embeddings carrying syntactic semantic information shown table since trainable parameters system associated word embeddings poor results suggest use considerably training data natural_language_processing azb natural_language_processing bac natural_language_processing cbd natural_language_processing dce natural_language_processing edf natural_language_processing feg natural_language_processing gfh natural_language_processing hgi natural_language_processing ihj natural_language_processing jik natural_language_processing kjl natural_language_processing lkn natural_language_processing nlm natural_language_processing mno natural_language_processing omp natural_language_processing poq natural_language_processing qpr natural_language_processing rqs natural_language_processing srt natural_language_processing tsu natural_language_processing utv natural_language_processing vuw natural_language_processing wvx natural_language_processing xwy natural_language_processing yxz natural_language_processing zya natural_language_processing bza natural_language_processing cab natural_language_processing dbc natural_language_processing ecd natural_language_processing fde natural_language_processing gef natural_language_processing hfg natural_language_processing kjl natural_language_processing lkn natural_language_processing nlm natural_language_processing igh natural_language_processing jhi natural_language_processing kij natural_language_processing ljk natural_language_processing nkl natural_language_processing mln neural_networks azb neural_networks bac neural_networks cbd neural_networks dce neural_networks edf neural_networks feg neural_networks gfh neural_networks hgi neural_networks ihj neural_networks jik neural_networks qop neural_networks rpq neural_networks sqr neural_networks trs neural_networks ust neural_networks vtu neural_networks wuv neural_networks xvw neural_networks ywx neural_networks zxy neural_networks ayz neural_networks azb neural_networks bac n neural_networks qpr neural_networks rqs neural_networks srt neural_networks tsu neural_networks utv neural_networks vuw neural_networks wvx neural_networks xwy neural_networks yxz neural_networks zya neural_networks bza neural_networks cab neural_networks dbc neural_networks ecd neural_networks fde neural_networks gef neural_networks hfg neural_networks igh neural_networks jhi neural_networks kij neural_networks ljk neural_networks nkl neural_networks srt neural_networks tsu neural_networks utv neural_networks vuw neural_networks wvx neural_networks xwy neural_networks yxz neural_networks zya neural_networks bza neural_networks cab neural_networks dbc neural_networks ecd neural_networks fde neural_networks gef neural_networks hfg neural_networks igh neural_networks jhi neural_networks kij neural_networks ljk neural_networks nkl neural_networks srt neural_networks tsu neural_networks utv neural_networks vuw neural_networks wvx neural_networks xwy neural_networks yxz neural_networks zya neural_networks bza neural_networks cab neural_networks dbc neural_networks ecd neural_networks fde neural_networks gef neural_networks hfg neural_networks igh neural_networks jhi neural_networks kij neural_networks ljk neural_networks nkl neural_networks mln neural_networks onm neural_networks fde neural_networks gef neural_networks hfg neural_networks igh neural_networks jhi neural_networks kij neural_networks ljk language_model azb language_model bac language_model cbd language_model dce language_model edf language_model feg language_model gfh language_model hgi language_model ihj language_model jik language_model kjl language_model lkn language_model nlm language_model mno language_model omp language_model poq language_model qpr language_model rqs language_model srt language_model tsu language_model utv language_model vuw language_model wvx language_model xwy language language_model ljk language_model nkl language_model mln language_model onm language_model pmo language_model qop language_model pmo language_model vuw language_model wvx language_model xwy nlp language_model nlp language_model nlp language_model nlp language_model nlp language_model nlp language_model nlp language_model nlp language_model nlp language_model nlp language_model nlp language_model nlp language_model nlp language_model nlp language_model nlp language_model nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning_nlp_machine_learning natural_language_processing almost from scratch pos b chunk c ner d srl figure f score validation set yaxis versus number hidden units xaxis different tasks trained sentencelevel likelihood sll table for srl vary graph number hidden units second layer the scale adapted task we show standard deviation obtained runs different random initialization architecture picked hidden units pos chunk ner srl following nlp scratch philosophy now describe dramatically improve embeddings using large unlabeled data sets we use improved embeddings initialize word lookup_tables networks described section data sets our first english corpus entire english wikipedia we removed paragraphs containing nonroman characters mediawiki markups the resulting text tokenized using penn treebank tokenizer script the resulting data set contains million words as previous experiments use dictionary containing common words wsj processing capitals numbers again words outside dictionary replaced special âœrareâ word our second english corpus composed adding extra million words extracted reuters rcv lewis et al data set we also extended dictionary words adding common words reuters this useful order determine whether improvements can achieved increasing unlabeled data set size ranking criterion versus entropy criterion we used unlabeled data sets train language_models compute scores describing acceptability piece text these language_models large neural_networks using window approach described section figure as previous section trainable parameters located lookup_tables similar language_models already proposed bengio ducharme schwenk gauvain their goal estimate probability word given previous words sentence estimating conditional probabilities suggests crossentropy criterion similar described section because dictionary size large computing normalization term available we took november version available now available collobert weston bottou karlen kavukcuoglu and kuksa can extremely demanding sophisticated approximations required more importantly us neither work leads significant word embeddings reported shannon estimated entropy english language bits per character asking human subjects guess upcoming characters cover king give lower bound bits per character using subtle gambling approach meanwhile using simple word trigram model brown et al b reach bits per character teahan cleary obtain entropies low bits per character using variable length character ngrams the human subjects rely course knowledge language world can learn grammatical structure english language nature world leveraging bits per character separate human subjects simple ngram models since tasks certainly require high capacity models obtaining sufficiently small confidence intervals test set entropy may require prohibitively large training sets the entropy criterion lacks dynamical range numerical value largely determined frequent phrases in order learn syntax rare legal phrases less significant common phrases it therefore desirable define alternative training criteria we propose use pairwise ranking approach cohen et al we seek network computes higher score given legal phrase given incorrect phrase because ranking literature often deals information retrieval applications many authors define complex ranking criteria give weight ordering best ranking instances see burges et al clemencâon vayatis â however case want emphasize common phrase rare legal phrases therefore use simple pairwise criterion we consider window approach network described section figure parameters î outputs score f î x given window text x w dwin we minimize ranking criterion respect î î â â\210 xâ\210\210x â\210 wâ\210\210d maxn â\210 fî x fî x w o x set possible text windows dwin words coming training corpus d dictionary words x w denotes text window obtained replacing central word text window w dwin word w okanohara tsujii use related approach avoiding entropy criteria using binary classification approach correctincorrect phrase their work focuses using kernel classifier learning word embeddings smith eisner also propose contrastive criterion estimates likelihood data conditioned âœnegativeâ neighborhood they consider various data neighborhoods including sentences length dwin drawn ddwin their goal however perform well tagging task fully unsupervised data rather obtaining generic word embeddings useful tasks training language_models the language_model network trained stochastic gradient minimization ranking criterion sampling sentenceword pair s w iteration however klein manning describe rare example realistic unsupervised grammar induction using crossentropy approach binarybranching parsing trees forcing system generate hierarchical representation natural_language_processing almost from scratch since training times large scale systems counted weeks feasible try many combinations hyperparameters it also makes sense speed training time initializing new networks embeddings computed earlier networks in particular found expedient train succession networks using increasingly large dictionaries network initialized embeddings previous network successive dictionary sizes switching times chosen arbitrarily bengio et al provides detailed discussion yet poorly understood âœcurriculumâ process for purposes model selection use process âœbreedingâ the idea breeding instead trying full grid search possible values enough computing power search parameters analogy breeding biological cell lines within line child networks initialized embeddings parents trained increasingly rich data sets sometimes different parameters that suppose k processors much less possible set parameters one like try one chooses k initial parameter choices large set trains k processors in case possible parameters adjust learning rate î word embedding dimensions d number hidden units n hu input window size dwin one trains models online fashion certain amount time ie days selects best ones using validation set error rate that breeding decisions made basis value ranking criterion estimated validation set composed one million words held wikipedia corpus in next breeding iteration one chooses another set k parameters possible grid values permute slightly successful candidates previous round as many parameter choices can share weights can effectively continue online training retaining learning previous iterations very long training times make strategies necessary foreseeable future given computers ten times faster probably found uses data sets ten times bigger however say believe although ended particular choice parameters many choices almost equally good although perhaps others better full grid search in following subsections report results obtained two trained language_models the results achieved two models representative achieved networks trained full corpora â language_model lm window size dwin hidden layer n hu units the embedding layers dimensioned like supervised networks table model lm trained first english corpus wikipedia using successive dictionaries composed finally common wsj words the total training time four weeks â language_model lm dimensions it initialized embeddings lm trained additional three weeks second english corpus wikipediareuters using dictionary size words embeddings both networks produce much appealing word embeddings section table shows ten nearest neighbors randomly chosen query words lm model the syntactic collobert weston bottou karlen kavukcuoglu and kuksa france jesus xbox reddish scratched megabits austria god amiga greenish nailed octets belgium sati playstation bluish smashed mbs germany christ msx pinkish punched bits italy satan ipod purplish popped baud greece kali sega brownish crimped carats sweden indra psnumber greyish scraped kbits norway vishnu hd grayish screwed megahertz europe ananda dreamcast whitish sectioned megapixels hungary parvati geforce silvery slashed gbits switzerland grace capcom yellowish ripped amperes table word embeddings word lookup_table language_model neural network lm trained dictionary size for column queried word followed index dictionary higher means rare nearest neighbors using euclidean metric chosen arbitrarily semantic properties neighbors clearly related query word these results far satisfactory reported table embeddings obtained using purely supervised training benchmark nlp tasks semisupervised benchmark results semisupervised learning object much attention last years see chapelle et al previous semisupervised approaches nlp can roughly categorized follows â adhoc approaches rosenfeld feldman relation extraction â selftraining approaches ueffing et al machine translation mcclosky et al parsing these methods augment labeled training set examples unlabeled data set using labels predicted model transductive approaches joachims text classification can viewed refined form selftraining â parameter sharing approaches ando zhang suzuki isozaki ando zhang propose multitask approach jointly train models sharing certain parameters they train pos ner models together language_model trained million words consisting predicting words given surrounding tokens suzuki isozaki embed generative model hidden markov model inside crf pos chunking ner the generative model trained one billion words these approaches seen linear counterpart work using multilayer models vastly expands parameter sharing opportunities see section our approach simply consists initializing word lookup_tables supervised networks embeddings computed language_models supervised training performed section in particular supervised training stage free modify lookup_tables this sequential approach computationally convenient separates lengthy training natural_language_processing almost from scratch approach pos chunk ner srl pwa f f f benchmark systems nnwll nnsll nnwlllm nnslllm nnwlllm nnslllm table comparison generalization performance benchmark nlp systems nn approach pos chunking ner srl tasks we report results wordlevel loglikelihood wll sentencelevel loglikelihood sll we report lmn performance networks trained language_model embeddings table generalization performance reported perword accuracy pwa pos f score tasks language_models relatively fast training supervised networks once language_models trained can perform multiple experiments supervised networks relatively short time note procedure clearly linked semisupervised deep learning procedures hinton et al bengio et al weston et al table clearly shows simple initialization significantly boosts generalization performance supervised networks task it worth mentioning larger language_model led even better performance this suggests still take advantage even bigger unlabeled data sets ranking language there large agreement nlp community syntax necessary prerequisite semantic role labeling gildea palmer this stateoftheart semantic role labeling systems thoroughly exploit multiple parse_trees the parsers charniak collins contain considerable prior information syntax one can think kind informed preprocessing our system use parse_trees attempt learn information unlabeled data set it therefore legitimate question whether ranking criterion conceptual capability capture rich hierarchical information at first glance ranking task appears unrelated induction probabilistic grammars underly standard parsing algorithms the lack hierarchical representation seems fatal flaw chomsky however ranking closely related alternative description language structure operator grammars harris instead directly studying structure sentence harris defines algebraic structure space sentences starting couple elementary sentence forms sentences described successive application sentence transformation operators the sentence structure revealed side effect successive transformations sentence transformations can also semantic interpretation collobert weston bottou karlen kavukcuoglu and kuksa in spirit structural linguistics harris describes procedures discover sentence transformation operators leveraging statistical regularities language such procedures obviously useful machine_learning approaches in particular proposes test decide whether two sentences forms semantically related transformation operator he first defines ranking criterion harris section âœstarting convenience short sentence forms say abc choose particular word choice classes say bqcq except one case a every pair members ai aj word class ask sentence formed one members aibqcq compares acceptability sentence formed member ajbqcqâ these gradings used compare sentence forms âœit now turns given graded ntuples words particular sentence form can find sentences forms word classes ntuples words produce grading sentencesâ this indication two sentence forms exploit common words syntactic function possibly meaning this observation forms empirical basis construction operator grammars describe realworld natural_languages english therefore solid reasons believe ranking criterion conceptual potential capture strong syntactic semantic information on hand structure language_models probably restrictive goals current approach exploits word embeddings discovered training multitask learning it generally accepted features trained one task can useful related tasks this idea already exploited previous section certain language_model features namely word embeddings used initialize supervised networks multitask learning mtl leverages idea systematic way models tasks interests jointly trained additional linkage trainable parameters hope improving generalization error this linkage can take form regularization term joint cost function biases models towards common representations a much simpler approach consists models share certain parameters defined priori multitask learning long history machine_learning neural_networks caruana gives good overview past efforts joint decoding versus joint training multitask approaches necessarily involve joint training for instance modern speech recognition systems use bayes rule combine outputs acoustic model trained speech data language_model trained phonetic textual corpora jelinek this joint decoding approach successfully applied structurally complex nlp tasks sutton mccallum b obtain improved results combining predictions independently trained crf models using joint decoding process test time requires sophisticated probabilistic natural_language_processing almost from scratch inference techniques on hand sutton mccallum obtain results somewhat stateoftheart using joint decoding srl syntactic parsing musillo merlo also describe negative result joint task joint decoding invariably works considering additional probabilistic dependency paths models therefore defines implicit supermodel describes tasks probabilistic framework separately training submodel makes sense training data blocks additional dependency paths sense dseparation pearl this implies without joint training additional dependency paths directly involve unobserved variables therefore natural idea discovering common internal representations across tasks requires joint training joint training relatively straightforward training sets individual tasks contain patterns different labels it sufficient train model computes multiple outputs pattern suddarth holden using scheme sutton et al demonstrate improvements pos tagging nounphrase chunking using jointly trained crfs however joint labeling requirement limitation data often available miller et al achieves performance improvements jointly training ner parsing relation extraction statistical parsing model the joint labeling requirement problem weakened using predictor fill missing annotations ando zhang propose setup works around joint labeling requirements they define linear models form fix w âš îx v âš îîx fi classifier ith task parameters wi vi notations îx îx represent engineered features pattern x matrix î maps îx features low dimensional subspace common across tasks each task trained using examples without joint labeling requirement the learning procedure alternates optimization wi vi task optimization î minimize average loss examples tasks the authors also consider auxiliary unsupervised tasks predicting substructures they report excellent results several tasks including pos ner multitask benchmark results table reports results obtained jointly trained models pos chunk ner srl tasks using setup section we trained jointly pos chunk ner using window approach network as mentioned earlier srl can trained sentence approach network due longrange dependencies related verb predicate we thus performed additional experiments four tasks trained using sentence approach network in cases models share lookup_table parameters the parameters first linear layers shared window approach case see figure first convolution layer parameters shared sentence approach networks for window approach best results obtained enlarging first hidden layer size n hu chosen validation order account shared responsibilities we used architecture srl sentence approach network the word embedding dimension kept constant d order reuse language_models section training achieved minimizing loss averaged across tasks this easily achieved stochastic gradient alternatively picking examples task applying parameters corresponding model including shared parameters note gives task equal weight since task uses training sets described table worth noticing collobert weston bottou karlen kavukcuoglu and kuksa lookup_table linear lookup_table linear hardtanh hardtanh linear task linear task m t ã â m t ã â xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxlt ltxxxxxxxxxxw w k xxxxxm ã â xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx n hu xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx n hu xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx n hut tags xxxxxxxxxxxxxxxxxxxxxxx n hut tags figure example multitasking nn task task two tasks trained window approach architecture presented figure lookup_tables well first hidden layer shared the last layer task specific the principle two tasks examples can come quite different data sets the generalization performance task measured using traditional testing data specified table fortunately none training test sets overlap across tasks it worth mentioning mtl can produce single unified network performs well tasks using sentence approach however unified network leads marginal improvements using separate network task important mtl task appears unsupervised learning word embeddings as explained simple computational considerations led us train pos chunking ner tasks using window approach the baseline results table also show using sentence approach pos chunking ner tasks yields performance improvement degradation window approach the next section shows can leverage known correlations tasks direct manner the temptation results far obtained staying almost true scratch philosophy we far avoided specializing architecture task disregarding lot useful priori we basic preprocessing raw input words described section hence âœalmostâ title article a completely scratch approach presumably know anything words work letters taken extreme speech optical character recognition humans natural_language_processing almost from scratch approach pos chunk ner srl pwa f f f benchmark systems window approach nnslllm â nnslllmmtl â sentence approach nnslllm nnslllmmtl table effect multitasking neural architectures we trained pos chunk ner mtl way window sentence network approaches srl included sentence approach joint training as baseline show previous results window approach system well additional results sentence approach system trained separately task benchmark system performance also given comparison approach pos chunk ner srl pwa f f benchmark systems nnslllm nnslllmsuffix â â â nnslllmgazetteer â â â nnslllmpos â â nnslllmchunk â â â table comparison generalization performance benchmark nlp systems neural_networks nns using increasing taskspecific engineering we report results obtained network trained without extra taskspecific features section extra taskspecific features described section the pos network trained two character word suffixes ner network trained using small conll gazetteer chunk ner networks trained additional pos features finally srl network trained additional chunk features nlp knowledge we shown thanks large unlabeled data sets generic neural_networks can still achieve close stateoftheart performance discovering useful features this section explores happens increase level taskspecific engineering systems incorporating common techniques nlp literature we often obtain improvements these figures useful quantify far went leveraging large data sets instead relying priori knowledge collobert weston bottou karlen kavukcuoglu and kuksa suffix features word suffixes many western languages strong predictors syntactic function word therefore can benefit pos system for instance ratnaparkhi uses inputs representing word suffixes prefixes four characters we achieve pos task adding discrete word features section representing last two characters every word the size suffix dictionary this led small improvement pos performance table row nnslllmsuffix we also tried suffixes obtained porter stemmer obtained performance using two character suffixes gazetteers stateoftheart ner systems often use large dictionary containing well known named entities eg florian et al we restricted gazetteer provided conll challenge containing locations person names organizations miscellaneous entities we trained ner network additional word features indicating feature âœonâ âœoffâ whether word found gazetteer one four categories the gazetteer includes words also chunks words if sentence chunk found gazetteer words chunk corresponding gazetteer feature turned âœonâ the resulting system displays clear performance improvement table row nnslllmgazetteer slightly outperforming baseline a plausible explanation large boost network using language_model gazetteers include word chunks use word representation language_model for example âœunitedâ âœbicycleâ seen separately likely nonentities âœunited bicycleâ might entity catching require higher level representations language_model cascading when one considers related tasks reasonable assume tags obtained one task can useful taking decisions tasks conventional nlp systems often use features obtained output preexisting nlp systems for instance shen sarkar describe chunking system uses pos tags input florian et al describes ner system whose inputs include pos chunk tags well output two ner classifiers stateoftheart srl systems exploit parse_trees gildea palmer punyakanok et al related chunk tags built using pos tags charniak collins table reports results obtained chunk ner tasks adding discrete word features section representing pos tags in order facilitate comparisons instead using accurate tags pos network use task pos tags provided corresponding conll challenge we also report results obtained srl task adding word features representing chunk tags also provided conll challenge we consistently obtain moderate improvements ensembles constructing ensembles classifiers proven way trade computational efficiency generalization performance bell et al therefore surprising many nlp systems achieve stateoftheart performance combining outputs multiple classifiers for instance kudo natural_language_processing almost from scratch approach pos chunk ner pwa f f benchmark systems nnslllmpos worst nnslllmpos mean nnslllmpos best nnslllmpos voting ensemble nnslllmpos joined ensemble table comparison generalization performance pos chunk ner tasks networks obtained using combining ten training runs different initialization matsumoto use ensemble classifiers trained different tagging conventions see section winning challenge course legitimate objective yet often difficult figure ideas responsible stateoftheart performance large ensemble because neural_networks nonconvex training runs different initial parameters usually give different solutions table reports results obtained chunk ner task ten training runs random initial parameters voting ten network outputs per tag basis âœvoting ensembleâ leads small improvement average network performance we also tried sophisticated ensemble approach ten network output scores sentencelevel likelihood combined additional linear layer fed new sentencelevel likelihood the parameters combining layers trained existing training set keeping ten networks fixed âœjoined ensembleâ this approach improve simple voting these ensembles come course expense ten fold increase running time on hand multiple training times improved using smart sampling strategies neal we can also observe performance variability among ten networks large the local minima found training algorithm usually good local minima thanks oversized parameter space noise induced stochastic gradient procedure lecun et al in order reduce variance experimental results always use initial parameters networks trained task except course results reported table parsing gildea palmer punyakanok et al offer several arguments suggesting syntactic parsing necessary prerequisite srl task the conll srl benchmark task provides parse_trees computed using charniak collins parsers stateoftheart systems often exploit additional parse_trees k top ranking parse_trees koomen et al haghighi et al in contrast srl networks far use parse_trees they rely instead internal representations transferred language_model trained objective function captures collobert weston bottou karlen kavukcuoglu and kuksa level s np language_model language_model language_model language_model language_model language_model language_model language_model language_model language_model language_model language_model language_model language_model language_model language_model the luxury auto maker bnp inp inp enp np last year bnp enp vp sold svp np cars bnp enp pp spp np us bnp enp level s the luxury auto maker last year o o o o o o vp sold cars bvp ivp evp pp us bpp ipp epp level s the luxury auto maker last year o o o o o o vp sold cars us bvp ivp ivp ivp ivp evp figure charniak parse_tree sentence âœthe luxury auto maker last year sold cars usâ level original tree levels obtained successively collapsing terminal tree branches for level words receive tags describing segment associated corresponding leaf all words receive tag âœoâ level example lot syntactic information see section it therefore legitimate question whether approach acceptable lightweight replacement parse_trees we answer question providing parse_tree information additional input features system we limited charniak parse_tree provided conll data considering node syntactic parse_tree assigns label segment parsed sentence propose way feed partially labeled segmentation network additional lookup_tables each lookup_tables encode labeled segments parse_tree level certain depth the labeled segments fed network following iobes tagging scheme see sections as different phrase labels wsj additional treerelated lookup_tables entries ã corresponding ibes segment tags plus extra o tag we call level information associated leaves original charniak parse_tree the lookup_table level encodes corresponding iobes phrase tags words we obtain levels repeatedly trimming leaves shown figure we labeled âœoâ words belonging root node âœsâ words sentence root trimmed experiments performed using lm language_model using network architectures see table using additional lookup_tables dimension parse_tree level table reports performance improvements obtained providing increasing levels parse in recent work collobert propose extension approach generation full syntactic parse_trees using recurrent version architecture natural_language_processing almost from scratch approach srl valid test benchmark system six parse_trees benchmark system top charniak parse_tree â nnslllm nnslllmcharniak level nnslllmcharniak levels nnslllmcharniak levels nnslllmcharniak levels nnslllmcharniak levels nnslllmchunk â nnslllmpt â table generalization performance srl task nn architecture compared benchmark system we show performance system fed different levels depth charniak parse_tree we report previous results architecture parse_tree baseline koomen et al report test validation performance using six parse_trees well validation performance using top charniak parse_tree for comparison purposes hence also report validation performance finally report performance chunk feature compare level feature pt obtained network tree information level alone increases f score almost additional levels yield diminishing returns the top performance reaches f score this far stateoftheart system note uses six parse_trees instead one koomen et al also report f score validation set using charniak parse_tree using first three parse_tree levels reach performance validation set these results corroborate findings nlp literature gildea palmer punyakanok et al showing parsing important srl task we also reported table previous performance obtained chunk feature see table it surprising observe adding chunking features semantic role labeling network performs significantly worse adding features describing level charniak parse_tree table indeed ignore label prefixes âœbiesâ defining segmentation parse_tree leaves level chunking identical labeling however parse_trees identify leaf sentence segments often smaller identified chunking tags shown hollingshead et al instead relying charniak parser chose train second chunking network identify segments delimited leaves penn treebank parse_trees level our network achieved f score task call pt evaluated charniak performance task as shown table feeding as hollingshead et al consider sentence chunk labels âœnp they vp starting buy np growth stocksâ the parse_tree can written âœs np they vp vp starting s vp vp buy np growth stocksâ the tree leaves segmentation thus given âœnp they vp vp starting vp vp buy np growth stocksâ collobert weston bottou karlen kavukcuoglu and kuksa pt prediction srl system gives similar performance using charniak predictions consistently better chunk feature word representations we described induced useful word embeddings applying architecture language_modeling task trained using large amount unlabeled text data these embeddings improve generalization performance tasks section the literature describes ways induce word representations mnih hinton proposed related language_model approach inspired restricted boltzmann machines however word representations perhaps commonly inferred ngram language_modeling rather purely continuous language_models one popular approach brown clustering algorithm brown et al builds hierarchical word clusters maximizing bigramâs mutual information the induced word representation used success wide variety nlp tasks including pos schutze ner miller et al ratinov roth parsin â g koo et al other related approaches exist like phrase clustering lin wu shown work well ner finally huang yates recently proposed smoothed language_modeling approach based hidden markov model success pos chunking tasks while comparison word representations beyond scope paper rather fair question quality word embeddings compared popular nlp approach in section report comparison word embeddings brown clusters used features neural network architecture we report baseline previous results word embeddings finetuned task we also report performance embeddings kept fixed taskspecific training since convex machine_learning algorithms common practice nlp finally report performances convex version architecture for convex experiments considered linear version neural_networks instead several linear layers interleaved nonlinearity while always picked sentence approach srl consider window approach particular convex setup sentence approach network see figure includes max layer having one linear layer neural network enough make architecture convex lookuptables discrete feature must also fixed the wordlookup_table simply fixed embeddings obtained language_model lm all discrete feature lookuptables caps pos brown clusters fixed standard sparse representation using notation introduced section ltwk lookuptable k th discrete feature wk â\210\210 r dk ãdk representation discrete input w obtained ltwk w hwk w âââ index w âââ t training architecture convex setup sentencelevel likelihood corresponds training crf in respect convex experiments show performance word embeddings classical nlp framework following ratinov roth koo et al setups generated brown clusters using implementation liang to make comparison fair clusters first induced concatenation wikipedia reuters data sets section available natural_language_processing almost from scratch approach pos chunk ner srl pwa f f f nonconvex approach lm nonlinear nn lm nonlinear nn fixed embeddings brown clusters nonlinear nn k words brown clusters nonlinear nn words convex approach lm linear nn fixed embeddings brown clusters linear nn k words brown clusters linear nn words table generalization performance neural network architecture trained language_model lm word embeddings word representations derived brown clusters as networks fed capitalization feature additionally pos using word suffix size feature chunk fed pos ner uses conll gazetteer srl fed levels â charniak parse_tree well verb position feature we report performance convex nonconvex architectures hidden units tasks additional hidden units layer srl we also provide results brown clusters induced k word dictionary well brown clusters induced words given tasks training largest language_model lm using k word dictionary this dictionary covers words training set task to cover last augmented dictionary missing words reaching k words induced brown clusters using concatenation wsj wikipedia reuters the brown clustering approach hierarchical generates binary tree clusters each word vocabulary assigned node tree features extracted tree considering path root node containing word interest following ratinov roth picked features path prefixes size in nonconvex experiments fed four brown cluster features architecture using four different lookup_tables replacing word lookup_table the size lookup_tables chosen validation in convex case used classical sparse representation discrete feature we first report table generalization performance best nonconvex networks trained lm language_model brown cluster features our embeddings perform least well brown clusters results mitigated convex setup for tasks going nonconvex better word representation types in general âœfinetuningâ embeddings task also gives extra boost finally using better word coverage brown clusters âœall wordsâ instead âœk wordsâ table help more complex features possibly combined instead using nonlinear model for instance turian et al performed comparison brown clusters embeddings trained spirit additional features combining labels tokens we believe however reach embedding performance there several differences trained models might explain firstly may experienced difficulties train dimensional collobert weston bottou karlen kavukcuoglu and kuksa task features pos suffix size chunk pos ner conll gazetteer pt pos srl pt verb position table features used senna implementation task in addition tasks use âœlow caps wordâ âœcapsâ features task benchmark senna part speech pos accuracy chunking chunk f named entity recognition ner f parse_tree level pt f semantic role labeling srl f table performance engineered sweet spot senna various tagging tasks the pt task replicates sentence segmentation parse_tree leaves the corresponding benchmark score measures quality charniak parse_tree leaves relative penn treebank gold parse_trees type comparison taken care combining given feature different word representations might effect word representation engineering sweet spot we implemented standalone version architecture written c language we gave name âœsennaâ semanticsyntactic extraction using neural network architecture resulting system the parameters architecture ones described table all networks trained separately task using sentencelevel likelihood sll the word embeddings initialized lm embeddings finetuned task we summarize features used implementation table report performance achieved task table the runtime version contains lines c code runs less mb memory needs less millisecond per word compute tags table compares tagging speeds system available stateoftheart systems toutanova et al pos tagger shen et al pos tagger koomen et al srl embeddings k distinct words using comparatively small training set rcv m words unlikely contain enough instances rare words secondly predict correctness final word window instead center word turian et al effectively restricting model unidirectional prediction finally fine tune embeddings unsupervised training available available we picked version may available natural_language_processing almost from scratch pos system ram mb time s toutanova et al shen et al senna srl system ram mb time s koomen et al senna machine_learning azb machine_learning bac machine_learning cbd machine_learning dce machine_learning edf machine_learning feg machine_learning gfh machine_learning ecd machine_learning fde machine_learning gef machine_learning hfg machine_learning igh machine_learning jhi machine_learning kij machine_learning ljk machine_learning nkl machine_learning mln machine_learning onm machine_learning pmo machine_learning qop machine_learning rpq machine_learning sqr machine_learning trs machine_learning ust machine_learning vtu machine_learning wuv machine_learning xvw machine_learning ywx machine_learning zxy machine_learning ayz machine_learning azb machine_learning bac machine_learning cbd machine_learning dce machine_learning edf machine_learning feg machine_learning gfh machine_learning hgi machine_learning ihj machine_learning jik machine_learning kjl machine_learning lkn machine_learning nlm machine_learning mno machine_learning omp machine_learning poq machine_learning qpr machine_learning rqs machine_learning srt machine_learning tsu machine_learning utv machine_learning vuw machine_learning wvx machine_learning xwy machine_learning yxz machine_learning zya machine_learning bza machine_learning cab machine_learning dbc machine_learning ecd machine_learning fde machine_learning onm table runtime speed memory consumption comparison stateoftheart systems approach senna we give runtime seconds running pos srl taggers respective testing sets memory usage reported megabytes system all programs run single ghz intel core the pos taggers run sun java large enough memory allocation reach top tagging speed the beam size shen tagger set recommended paper regardless implementation differences clear neural_networks run considerably faster they also require much less memory our pos srl tagger runs mb mb ram respectively the shen toutanova taggers slow significantly java machine given less gb mb ram respectively koomen tagger requires least gb ram we believe number reasons explain speed advantage system first system uses rather simple input features therefore avoids nonnegligible computation time associated complex handcrafted features secondly network computations dense matrixvector operations in contrast systems rely great number sparse features experience memory latencies traversing sparse data structures finally compact implementation selfcontained since rely outputs disparate nlp system suffer communication latency issues critical discussion although believe contribution represents step towards âœnlp scratchâ objective keenly aware goal means can criticized the main criticism goal can summarized follows over years nlp community developed considerable expertise engineering effective nlp features why forget painfully acquired expertise instead painfully acquire skills required train large neural_networks as mentioned introduction observe single nlp task really covers goals nlp therefore believe taskspecific engineering ie generalize tasks desirable but also recognize much neural_networks owe previous nlp taskspecific research the main criticism means easier address why choose rely twenty year old technology namely multilayer neural_networks we simply attracted ability discover hidden representations using stochastic learning algorithm scales linearly available collobert weston bottou karlen kavukcuoglu and kuksa number examples most neural network technology necessary work described ten years ago eg le cun et al however decided ten years ago train language_model network lm using vintage computer training nearing completion today training algorithms scale linearly able benefit tremendous progress computer hardware conclusion we presented multilayer neural network architecture can handle number nlp tasks speed accuracy the design system determined desire avoid taskspecific engineering much possible instead rely large unlabeled data sets let training algorithm discover internal representations prove useful tasks interest using strong basis engineered fast efficient âœall purposeâ nlp tagger hope will prove useful community acknowledgments we acknowledge persistent support nec research effort we thank yoshua bengio samy bengio eric cosatto vincent etter hanspeter graf ralph grishman vladimir vapnik useful feedback comments appendix a neural network gradients we consider neural network fîâ parameters î we maximize likelihood minimize ranking criterion respect parameters î using stochastic gradient by negating likelihood now assume corresponds minimize cost cfîâ respect î following classical âœbackpropagationâ derivations lecun rumelhart et al modular approach shown bottou feedforward neural network l layers like ones shown figure figure can seen composition functions f l î â corresponding layer l fîâ f l î f lâ\210 î f î â partitioning parameters network respect layers â l â l write î î î l î l we now interested computing gradients cost respect î l applying chain rule generalized vectors obtain classical backpropagation recursion â\210c â\210îl â\210 f l î â\210îl â\210c â\210 f l î â\210c â\210 f lâ\210 î â\210 f l î â\210 f lâ\210 î â\210c â\210 f l î in words first initialize recursion computing gradient cost respect last layer output â\210câ\210 f l î then layer l computes gradient respect parameters natural_language_processing almost from scratch given gradient coming output â\210câ\210 f l î to perform backpropagation also computes gradient respect inputs shown we now derive gradients layer used paper a lookup_table layer given matrix parameters î w word discrete feature indices w t layer outputs matrix f l î w t l hwi w hwi w hwi wt the gradients weights hwi given â\210c â\210hwi â\210 âtât w ti h â\210c â\210 f l î this sum equals zero index lookup_table corresponds word sequence in case th column w need updated as lookup_table layer always first layer need compute gradients respect inputs a linear layer given parameters î l wl b l input vector f lâ\210 î output given f l î wl f lâ\210 î b l the gradients respect parameters obtained â\210c â\210wl â\210c â\210 f l î h f lâ\210 î it â\210c â\210b l â\210c â\210 f l î gradients respect inputs computed â\210c â\210 f lâ\210 î h wl it â\210c â\210 f l î a convolution layer given input matrix f lâ\210 î convolution layer f l î â applies linear layer operation successively window hf lâ\210 î dwin t â t â t size dwin using gradients parameters thus given summing windows â\210c â\210wl t â\210 t h â\210c â\210 f l î t h hf lâ\210 î dwin t it â\210c â\210b l t â\210 t h â\210c â\210 f l î t after initializing input gradients â\210câ\210 f lâ\210 î zero iterate windows â t â t leading accumulation h â\210c â\210 f lâ\210 î dwin t h wl it h â\210c â\210 f l î t we denote âœâ accumulation operation collobert weston bottou karlen kavukcuoglu and kuksa a max layer given matrix f lâ\210 î max layer computes h f l î max t h hf lâ\210 î t ai argmax t h hf lâ\210 î t â\210i ai stores index largest value we need compute gradient respect inputs layer parameters the gradient given h â\210c â\210 f lâ\210 î t h h â\210c â\210 f l î t t ai otherwise a hardtanh layer given vector f lâ\210 î definition hardtanh get â\210c â\210 f lâ\210 î ï ïïïï ïïïï h f lâ\210 î â\210 h â\210c â\210 f l î â\210 h f lâ\210 î h f lâ\210 î ignore nondifferentiability points a wordlevel loglikelihood the network outputs score fî tag indexed following y true tag given example stochastic score minimize can written cfî logadd j fî j â\210 fî y considering definition logadd gradient respect fî given â\210c â\210 fî e fî â\210k e fî k â\210iy â\210i a sentencelevel loglikelihood the network outputs matrix element f î gives score tag word t given tag sequence y t input sequence x t maximize likelihood corresponds minimizing score cfîa logadd â\210 j t sx t j t îëœ z clogadd â\210sx t y t îëœ sx t y t îëœ t â\210 t a y tâ\210 y t fî y t t natural_language_processing almost from scratch we first initialize gradients zero â\210c â\210 f î â\210it â\210c â\210a j â\210i j we accumulate gradients second part cost â\210sx t y t îëœ gives â\210c â\210 f î y t t â\210c â\210a y tâ\210 y t â\210t we now need accumulate gradients first part cost clogadd we differentiate clogadd applying chain rule recursion first initialize recursion â\210clogadd â\210ît e ît â\210k e ît k â\210i we compute iteratively â\210clogadd â\210îtâ\210i â\210 j â\210clogadd â\210îtj e îtâ\210ia j â\210k e îtâ\210ka k j step t recursion accumulate gradients respect inputs fî transition scores a j â\210c â\210 f î â\210clogadd â\210îti â\210îti â\210 f î â\210clogadd â\210îti â\210c â\210a j â\210clogadd â\210îtj â\210îtj â\210a j â\210clogadd â\210îtj e îtâ\210ia j â\210k e îtâ\210ka k j a ranking criterion 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linearthreshold algorithm in machine_learning pages â a mccallum wei li early results named entity recognition conditional random fields feature induction webenhanced lexicons in conference north american chapter association computational_linguistics human language technologies naaclhlt pages â d mcclosky e charniak m johnson effective selftraining parsing conference north american chapter association computational_linguistics human language technologies naaclhlt r mcdonald k crammer f pereira flexible text segmentation structured multilabel classification in conference human language technology empirical methods natural_language_processing hltemnlp pages â s miller h fox l ramshaw r weischedel a novel use statistical parsing extract information text applied natural_language_processing conference anlp s miller j guinness a zamanian name tagging word clusters discriminative training in conference north american chapter association computational_linguistics human language technologies naaclhlt pages â a mnih g e hinton three new graphical models statistical language_modelling in international conference machine_learning icml pages â g musillo p merlo robust parsing proposition bank romand robust methods analysis natural_language data r m neal bayesian learning neural_networks number lecture notes statistics springerverlag new york d okanohara j tsujii a discriminative language_model pseudonegative samples meeting association computational_linguistics acl pages â m palmer d gildea p kingsbury the proposition bank an annotated corpus semantic roles computational_linguistics â j pearl probabilistic reasoning intelligent systems morgan kaufman san mateo d c plaut g e hinton learning sets filters using backpropagation computer speech language â m f porter an algorithm suffix stripping program â collobert weston bottou karlen kavukcuoglu and kuksa s pradhan w ward k hacioglu j martin d jurafsky shallow semantic parsing using support vector machines conference north american chapter association computational_linguistics human language technologies naaclhlt s pradhan k hacioglu w ward j h martin d jurafsky semantic role chunking combining complementary syntactic views in conference computational natural_language learning conll pages â v punyakanok d roth w yih the necessity syntactic parsing semantic role labeling in international joint conference artificial intelligence ijcai pages â l r rabiner a tutorial hidden markov models selected applications speech recognition proceedings ieee â l ratinov d roth design challenges misconceptions named entity recognition in conference computational natural_language learning conll pages â association computational_linguistics a ratnaparkhi a maximum entropy model partofspeech tagging in conference empirical methods natural_language_processing emnlp pages â b rosenfeld r feldman using corpus statistics entities improve semisupervised relation extraction web meeting association computational_linguistics acl pages â d e rumelhart g e hinton r j williams learning internal representations backpropagating errors in de rumelhart j l mcclelland editors parallel distributed processing explorations microstructure cognition volume pages â mit press h schutze distributional partofspeech tagging in â meeting association computational_linguistics acl pages â h schwenk j l gauvain connectionist language_modeling large vocabulary continuous speech recognition in international conference acoustics speech signal processing icassp pages â f sha f pereira shallow parsing conditional random fields in conference north american chapter association computational_linguistics human language technologies naaclhlt pages â c e shannon prediction entropy printed english bell systems technical journal â h shen a sarkar voting multiple data representations text chunking advances artificial intelligence pages â l shen g satta a k joshi guided learning bidirectional sequence classification in meeting association computational_linguistics acl natural_language_processing almost from scratch n a smith j eisner contrastive estimation training loglinear models unlabeled data in meeting association computational_linguistics acl pages â s c suddarth a d c holden symbolicneural systems use hints developing complex systems international journal manmachine studies â x sun lp morency d okanohara j tsujii modeling latentdynamic shallow parsing latent conditional model improved inference in international conference computational_linguistics coling pages â c sutton a mccallum joint parsing semantic role labeling in conference computational natural_language conll pages â c sutton a mccallum composition conditional random fields transfer learning conference human language technology empirical methods natural_language_processing hltemnlp pages â b c sutton a mccallum k rohanimanesh dynamic conditional random fields factorized probabilistic models labeling segmenting sequence data journal machine_learning research jmlr â j suzuki h isozaki semisupervised sequential labeling segmentation using gigaword scale unlabeled data in conference north american chapter association computational_linguistics human language technologies naaclhlt pages â w j teahan j g cleary the entropy english using ppmbased models in data compression conference dcc pages â ieee computer society press k toutanova d klein c d manning y singer featurerich partofspeech tagging cyclic dependency network in conference north american chapter association computational_linguistics human language technologies naaclhlt j turian l ratinov y bengio word representations a simple general method semisupervised learning in meeting association computational_linguistics acl pages â n ueffing g haffari a sarkar transductive learning statistical machine translation in meeting association computational_linguistics acl pages â a waibel t hanazawa g hinton k shikano kj lang phoneme recognition using timedelay neural_networks ieee transactions acoustics speech signal processing â j weston f ratle r collobert deep learning via semisupervised embedding in international conference machine_learning icml pages â natural_language_processing from wikipedia free encyclopedia jump navigationjump search not confused nonlinear programming not confused neurolinguistic programming this article language_processing computers for processing language human brain see language_processing brain an automated online assistant providing customer service web page example application natural_language_processing major component natural_language_processing nlp subfield linguistics computer science information engineering artificial intelligence concerned interactions computers human natural_languages particular program computers process analyze large amounts natural_language data challenges natural_language_processing frequently involve speech recognition natural_language understanding natural_language generation contents history rulebased vs statistical nlp major evaluations tasks syntax semantics discourse speech dialogue see also references further reading history the history natural_language_processing nlp generally started s although work can found earlier periods in alan turing published article titled computing machinery intelligence proposed now called turing test criterion intelligenceclarification needed the georgetown experiment involved fully automatic translation sixty russian sentences english the authors claimed within three five years machine translation solved problem however real progress much slower alpac report found tenyearlong research failed fulfill expectations funding machine translation dramatically reduced little research machine translation conducted late s first statistical machine translation systems developed some notably successful natural_language_processing systems developed s shrdlu natural_language system working restricted blocks worlds restricted vocabularies eliza simulation rogerian psychotherapist written joseph weizenbaum using almost information human thought emotion eliza sometimes provided startlingly humanlike interaction when patient exceeded small knowledge base eliza might provide generic response example responding my head hurts why say head hurts during s many programmers began write conceptual ontologies structured realworld information computerunderstandable data examples margie schank sam cullingford pam wilensky talespin meehan qualm lehnert politics carbonell plot units lehnert during time many chatterbots written including parry racter jabberwacky up s natural_language_processing systems based complex sets handwritten rules starting late s however revolution natural_language_processing introduction machine_learning algorithms language_processing this due steady increase computational power see moores law gradual lessening dominance chomskyan theories linguistics eg transformational grammar whose theoretical underpinnings discouraged sort corpus linguistics underlies machinelearning approach language_processing some earliestused machine_learning algorithms decision trees produced systems hard ifthen rules similar existing handwritten rules however partofspeech tagging introduced use hidden markov models natural_language_processing increasingly research focused statistical models make soft probabilistic decisions based attaching realvalued weights features making input data the cache language_models upon many speech recognition systems now rely examples statistical models such models generally robust given unfamiliar input especially input contains errors common realworld data produce reliable results integrated larger system comprising multiple subtasks many notable early successes occurred field machine translation due especially work ibm research successively complicated statistical models developed these systems able take advantage existing multilingual textual corpora produced parliament canada european union result laws calling translation governmental proceedings official languages corresponding systems government however systems depended corpora specifically developed tasks implemented systems often continues major limitation success systems as result great deal research gone methods effectively learning limited amounts data recent research increasingly focused unsupervised semisupervised learning algorithms such algorithms able learn data handannotated desired answers using combination annotated nonannotated data generally task much difficult supervised learning typically produces less accurate results given amount input data however enormous amount nonannotated data available including among things entire content world wide web can often make inferior results algorithm used low enough time complexity practical in s representation learning deep neural_networkstyle machine_learning methods became widespread natural_language_processing due part flurry results showing techniques can achieve stateoftheart results many natural_language tasks example language_modeling parsing many others popular techniques include use word embeddings capture semantic properties words increase endtoend learning higherlevel task eg question answering instead relying pipeline separate intermediate tasks eg partofspeech tagging dependency parsing in areas shift entailed substantial changes nlp systems designed deep neural networkbased approaches may viewed new paradigm distinct statistical natural_language_processing for instance term neural machine translation nmt emphasizes fact deep learningbased approaches machine translation directly learn sequencetosequence transformations obviating need intermediate steps word alignment language_modeling used statistical machine translation smt rulebased vs statistical nlp in early days many languageprocessing systems designed handcoding set rules writing grammars devising heuristic rules stemming since socalled statistical revolution late s mid s much natural_language_processing research relied heavily machine_learning the machinelearning paradigm calls instead using statistical inference automatically learn rules analysis large corpora plural form corpus set documents possibly human computer annotations typical realworld examples many different classes machinelearning algorithms applied naturallanguageprocessing tasks these algorithms take input large set features generated input data some earliestused algorithms decision trees produced systems hard ifthen rules similar systems handwritten rules common increasingly however research focused statistical models make soft probabilistic decisions based attaching realvalued weights input feature such models advantage can express relative certainty many different possible answers rather one producing reliable results model included component larger system systems based machinelearning algorithms many advantages handproduced rules the learning procedures used machine_learning automatically focus common cases whereas writing rules hand often obvious effort directed automatic learning procedures can make use statisticalinference algorithms produce models robust unfamiliar input eg containing words structures seen erroneous input eg misspelled words words accidentally omitted generally handling input gracefully handwritten rules generally creating systems handwritten rules make soft decisions extremely difficult errorprone timeconsuming systems based automatically learning rules can made accurate simply supplying input data however systems based handwritten rules can made accurate increasing complexity rules much difficult task in particular limit complexity systems based handcrafted rules beyond systems become unmanageable however creating data input machinelearning systems simply requires corresponding increase number manhours worked generally without significant increases complexity annotation process major evaluations tasks the following list commonly researched tasks natural_language_processing some tasks direct realworld applications others commonly serve subtasks used aid solving larger tasks though natural_language_processing tasks closely intertwined frequently subdivided categories convenience a coarse division given syntax grammar induction generate formal grammar describes languages syntax lemmatization the task removing inflectional endings return base dictionary form word also known lemma morphological segmentation separate words individual morphemes identify class morphemes the difficulty task depends greatly complexity morphology ie structure words language considered english fairly simple morphology especially inflectional morphology thus often possible ignore task entirely simply model possible forms word eg open opens opened opening separate words in languages turkish meitei highly agglutinated indian language however approach possible dictionary entry thousands possible word forms partofspeech tagging given sentence determine part speech pos word many words especially common ones can serve multiple parts speech for example book can noun book table verb book flight set can noun verb adjective can least five different parts speech some languages ambiguity othersdubious â discuss languages little inflectional morphology english particularly prone ambiguity chinese prone ambiguity tonal language verbalization such inflection readily conveyed via entities employed within orthography convey intended meaning parsing determine parse_tree grammatical analysis given sentence the grammar natural_languages ambiguous typical sentences multiple possible analyses in fact perhaps surprisingly typical sentence may thousands potential parses will seem completely nonsensical human there two primary types parsing dependency parsing constituency parsing dependency parsing focuses relationships words sentence marking things like primary objects predicates whereas constituency parsing focuses building parse_tree using probabilistic contextfree grammar pcfg see also stochastic grammar sentence breaking also known sentence boundary disambiguation given chunk text find sentence boundaries sentence boundaries often marked periods punctuation marks characters can serve purposes eg marking abbreviations stemming the process reducing inflected sometimes derived words root form eg close will root closed closing close closer etc word segmentation separate chunk continuous text separate words for language like english fairly trivial since words usually separated spaces however written languages like chinese japanese thai mark word boundaries fashion languages text segmentation significant task requiring knowledge vocabulary morphology words language sometimes process also used cases like bag words bow creation data mining terminology extraction the goal terminology extraction automatically extract relevant terms given corpus semantics lexical semantics what computational meaning individual words context distributional semantics how can learn semantic representations data machine translation automatically translate text one human language another this one difficult problems member class problems colloquially termed aicomplete ie requiring different types knowledge humans possess grammar semantics facts real world etc order solve properly named entity recognition ner given stream text determine items text map proper names people places type name eg person location organization although capitalization can aid recognizing named entities languages english information aid determining type named entity case often inaccurate insufficient for example first letter sentence also capitalized named entities often span several words capitalized furthermore many languages nonwestern scripts eg chinese arabic capitalization even languages capitalization may consistently use distinguish names for example german capitalizes nouns regardless whether names french spanish capitalize names serve adjectives natural_language generation convert information computer databases semantic intents readable human language natural_language understanding convert chunks text formal representations firstorder logic structures easier computer programs manipulate natural_language understanding involves identification intended semantic multiple possible semantics can derived natural_language expression usually takes form organized notations natural_language concepts introduction creation language metamodel ontology efficient however empirical solutions an explicit formalization natural_language semantics without confusions implicit assumptions closedworld assumption cwa vs openworld assumption subjective yesno vs objective truefalse expected construction basis semantics formalization optical character recognition ocr given image representing printed text determine corresponding text question answering given humanlanguage question determine answer typical questions specific right answer what capital canada sometimes openended questions also considered what meaning life recent works looked even complex questions recognizing textual entailment given two text fragments determine one true entails entails others negation allows either true false relationship extraction given chunk text identify relationships among named entities eg married sentiment analysis see also multimodal sentiment analysis extract subjective information usually set documents often using online reviews determine polarity specific objects it especially useful identifying trends public opinion social media purpose marketing topic segmentation recognition given chunk text separate segments devoted topic identify topic segment word sense disambiguation many words one meaning select meaning makes sense context for problem typically given list words associated word senses eg dictionary online resource wordnet discourse automatic summarization produce readable summary chunk text often used provide summaries text known type research papers articles financial section newspaper coreference resolution given sentence larger chunk text determine words mentions refer objects entities anaphora resolution specific example task specifically concerned matching pronouns nouns names refer the general task coreference resolution also includes identifying socalled bridging relationships involving referring expressions for example sentence he entered johns house front door front door referring expression bridging relationship identified fact door referred front door johns house rather structure might also referred discourse analysis this rubric includes number related tasks one task identifying discourse structure connected text ie nature discourse relationships sentences eg elaboration explanation contrast another possible task recognizing classifying speech acts chunk text eg yesno question content question statement assertion etc speech speech recognition given sound clip person people speaking determine textual representation speech this opposite text speech one extremely difficult problems colloquially termed aicomplete see in natural speech hardly pauses successive words thus speech segmentation necessary subtask speech recognition see in spoken languages sounds representing successive letters blend process termed coarticulation conversion analog signal discrete characters can difficult process also given words language spoken people different accents speech recognition software must able recognize wide variety input identical terms textual equivalent speech segmentation given sound clip person people speaking separate words a subtask speech recognition typically grouped texttospeech given text transform units produce spoken representation texttospeech can used aid visually impaired dialogue the first published work artificial intelligence published road marketed novel contains sixty million words see also road automated essay scoring biomedical text mining compound term processing computational_linguistics computerassisted reviewing controlled natural_language deep learning deep linguistic processing distributional semantics foreign language reading aid foreign language writing aid information extraction information retrieval language communication technologies language technology latent semantic indexing nativelanguage identification natural_language programming natural_language search query expansion reification linguistics speech processing spoken dialogue system textproofing text simplification transformer machine_learning model truecasing question answering wordvec references kongthon alisa sangkeettrakarn chatchawal kongyoung sarawoot haruechaiyasak choochart october â implementing online help desk system based conversational agent medes the international conference management emergent digital ecosystems france acm doi hutchins j the history machine translation nutshell pdfselfpublished source chomskyan linguistics encourages investigation corner cases stress limits theoretical models comparable pathological phenomena mathematics typically created using thought experiments rather systematic investigation typical phenomena occur realworld data case corpus linguistics the creation use corpora realworld data fundamental part machinelearning algorithms natural_language_processing in addition theoretical underpinnings chomskyan linguistics socalled poverty stimulus argument entail general learning algorithms typically used machine_learning successful language_processing as result chomskyan paradigm discouraged application models language_processing goldberg yoav a primer neural network models natural_language_processing journal artificial intelligence research â ian goodfellow yoshua bengio aaron courville deep learning mit press rafal jozefowicz oriol vinyals mike schuster noam shazeer yonghui wu exploring limits language_modeling do kook choe eugene charniak emnlp parsing language_modeling vinyals oriol et al nips winograd terry procedures representation data computer program understanding natural_language roger c schank robert p abelson scripts plans goals understanding an inquiry human knowledge structures mark johnson how statistical revolution changes computational_linguistics proceedings eacl workshop interaction linguistics computational_linguistics philip resnik four revolutions language log february klein dan christopher d manning natural_language grammar induction using constituentcontext model advances neural information processing systems kishorjit n vidya raj rk nirmal y sivaji b manipuri morpheme identification proceedings rd workshop south southeast asian natural_language_processing sanlp pages â coling mumbai december yucong duan christophe cruz formalizing semantic natural_language conceptualization existence archived wayback machine international journal innovation management technology pp versatile question answering systems seeing synthesis mittal et al ijiids pascal recognizing textual entailment challenge rte yi chucai tian yingli assistive text reading complex background blind persons camerabased document analysis recognition springer berlin heidelberg pp â citeseerx doi isbn further reading bates m models natural_language understanding proceedings national academy sciences united states america â doipnas pmc pmid steven bird ewan klein edward loper natural_language_processing python oreilly media isbn daniel jurafsky james h martin speech language_processing nd edition pearson prentice hall isbn mohamed zakaria kurdi natural_language_processing computational_linguistics speech morphology syntax volume istewiley isbn mohamed zakaria kurdi natural_language_processing computational_linguistics semantics discourse applications volume istewiley isbn christopher d manning prabhakar raghavan hinrich schãtze introduction information retrieval cambridge university press isbn official html pdf versions available without charge christopher d manning hinrich schãtze foundations statistical natural_language_processing the mit press isbn david m w powers christopher c r turk machine_learning natural_language springerverlag isbn wikimedia commons media related natural_language_processing vte natural_language_processing authority control edit wikidata lccn shndl globe letterssvglanguage portal categories natural_language_processingcomputational_linguisticsspeech recognitioncomputational fields studyartificial intelligence navigation menu not logged intalkcontributionscreate accountlog inarticletalkreadeditview historysearch search wikipedia main page contents featured content current events random article donate wikipedia wikipedia store interaction help about wikipedia community portal recent changes contact page tools what links related changes upload file special pages permanent link page information wikidata item cite page in projects wikimedia commons printexport create book download pdf printable version languages deutsch espaãol franãais Ãœêµì à à à à à à italiano ð ñƒññðºðð tiáºng viát äæ edit links this page last edited october utc text available creative commons attributionsharealike license additional terms may apply by using site agree terms use privacy policy wikipediaâ registered trademark wikimedia foundation inc nonprofit organization privacy policyabout wikipediadisclaimerscontact wikipediadeveloperscookie statementmobile viewwikimedia foundationpowered mediawiki natural_language_processing technology used aid computers understand humanâs natural_language itâs easy task teaching machines understand communicate leand romaf experienced software engineer passionate teaching people artificial intelligence systems work says âœin recent years significant breakthroughs empowering computers understand language just doâ this article will give simple introduction natural_language_processing can achieved what natural_language_processing natural_language_processing usually shortened nlp branch artificial intelligence deals interaction computers humans using natural_language the ultimate objective nlp read decipher understand make sense human languages manner valuable most nlp techniques rely machine_learning derive meaning human languages trending ai articles cheat sheets ai neural_networks machine_learning deep learning big data data science simplified part principles process getting started building realtime api infrastructure ai nlp workshop in fact typical interaction humans machines using natural_language_processing go follows a human talks machine the machine captures audio audio text conversion takes place processing textâs data data audio conversion takes place the machine responds human playing audio file what nlp used natural_language_processing driving force behind following common applications language translation applications google translate word processors microsoft word grammarly employ nlp check grammatical accuracy texts interactive voice response ivr applications used call centers respond certain usersâ requests personal assistant applications ok google siri cortana alexa why nlp difficult natural_language_processing considered difficult problem computer science itâs nature human language makes nlp difficult the rules dictate passing information using natural_languages easy computers understand some rules can highleveled abstract example someone uses sarcastic remark pass information on hand rules can lowlevelled example using character âœsâ signify plurality items comprehensively understanding human language requires understanding words concepts connected deliver intended message while humans can easily master language ambiguity imprecise characteristics natural_languages make nlp difficult machines implement how natural_language_processing works nlp entails applying algorithms identify extract natural_language rules unstructured language data converted form computers can understand when text provided computer will utilize algorithms extract meaning associated every sentence collect essential data sometimes computer may fail understand meaning sentence well leading obscure results for example humorous incident occurred s translation words english russian languages here biblical sentence required translation âœthe spirit willing flesh weakâ here result sentence translated russian back english âœthe vodka good meat rottenâ what techniques used nlp syntactic analysis semantic analysis main techniques used complete natural_language_processing tasks here description can used syntax syntax refers arrangement words sentence make grammatical sense in nlp syntactic analysis used assess natural_language aligns grammatical rules computer algorithms used apply grammatical rules group words derive meaning here syntax techniques can used lemmatization it entails reducing various inflected forms word single form easy analysis morphological segmentation it involves dividing words individual units called morphemes word segmentation it involves dividing large piece continuous text distinct units partofspeech tagging it involves identifying part speech every word parsing it involves undertaking grammatical analysis provided sentence sentence breaking it involves placing sentence boundaries large piece text stemming it involves cutting inflected words root form semantics semantics refers meaning conveyed text semantic analysis one difficult aspects natural_language_processing fully resolved yet it involves applying computer algorithms understand meaning interpretation words sentences structured here techniques semantic analysis named entity recognition ner it involves determining parts text can identified categorized preset groups examples groups include names people names places word sense disambiguation it involves giving meaning word based context natural_language generation it involves using databases derive semantic intentions convert human language"Output
tdm.mod <- TermDocumentMatrix(text_corpus_mod,control = list(wordLengths = c(4,Inf)))
#inspect(tdm2)
tdm.mod.word <- data.frame(word = tdm.mod$dimnames$Terms, frequency = tdm.mod$v,stringsAsFactors = FALSE) %>%
arrange(-frequency)
tdm.mod.wordOutput before concatenating n-grams
tdm <- TermDocumentMatrix(text_corpus,control = list(wordLengths = c(4,Inf)))
#inspect(tdm2)
tdm.word <- data.frame(word = tdm$dimnames$Terms, frequency = tdm$v,stringsAsFactors = FALSE) %>%
arrange(-frequency)
tdm.wordChart 1 for PPT
tdm.mod.word_top15 <- tdm.mod.word %>% top_n(15)## Selecting by frequencyggplot(tdm.mod.word_top15, aes(x=reorder(word,frequency) , y=frequency, fill=frequency))+
geom_bar(stat='identity')+
coord_flip()+
ylab('word')+
ggtitle('Word Count After Concatenating N-grams')+
scale_fill_gradient(low = 'deeppink4', high = 'deeppink1')
Chart 2 for PPT
tdm.word_top15 <- tdm.word %>% top_n(15)## Selecting by frequencyggplot(tdm.word_top15, aes(x=reorder(word,frequency) , y=frequency, fill=frequency))+
geom_bar(stat='identity')+
coord_flip()+
ylab('word')+
ggtitle('Word Count Before Concatenating N-grams')
WordCloud for PPT
wordcloud(tdm.mod.word$word, tdm.mod.word$frequency,random.order=FALSE, colors=brewer.pal(8, "Dark2"))
```