visual, auditory, verbal, and invisible suprasystems interacting in a congruent state

• Polymorphic behaviour: A sign (word, phrase) can have multiple senses in varied contexts. Resulting ambiguity in use: lexical, structural, scope (每個人都喜歡他的孩子), anaphora/deictic expressions (他上禮拜開始就應該待在這裡), etc.
• Paralinguistic and extralinguistic information merging in communicative settings: kinesics (body gestures), acoustic (vocal activity), silence, etc.

• Emotion, Mood, Feeling, Attitude, Temperament, Personal trait

• Local Grammar of Affect (Bednarek. 2008)
• Use pattern grammar (Hunston, 1999) in approaching evaluative (semantic) prosody (Lee and Hsieh, 2016).

• Chinese reader did not follow the segmentation rules, and tend to chunk single words into large information units (Liu et al. 2013)

• Multimodality affect state (paralinguistics)
• Lexical items vs lexical chunks/bundles
• Sense-sentiment concurrent processing
• Tracing varieties and changes

• Shift towards usage-based model
• word-meaning pair is fluid in nature, whose granularity (in terms of form length and meaning varieties) is influenced by its underlying ontology (paradigmatic dimension), surrounding context (syntagmatic dimension) and real-world application (pragmatic force).

不是我要說他，已經什麼都有了他還在那邊邱屁邱。別人我不敢說，這種人喔實在是 $#* :-(

tokens("不是我要說他，已經什麼都有了他還在那邊邱屁邱。我覺得喔這種人喔實在是 $#*")

## tokens from 1 document.
## text1 :
##  [1] "不是"   "我要"   "說"     "他"     "，"     "已經"   "什麼"  
##  [8] "都有"   "了"     "他"     "還在"   "那邊"   "邱"     "屁"    
## [15] "邱"     "。"     "我"     "覺得"   "喔"     "這種"   "人"    
## [22] "喔"     "實在是" "$"      "#"      "*"

• revisit the Chinese wordhood (and consider the word segmentation issue as
  wordhood annotation rather than processing).

• Wordnet
  • A fine-grained sense inventory and lexical relations database 中文詞彙網路

• Meanings are negotiated, subordinated to the sequential requirements of talk-in-interaction.

• What we typically regard as fixed meanings, such as those codified in a dictionary, are merely sedimented or stabilized structures that emerge as negotiated recurring patterns that have achieved cross-textual consistency.

• The lexicon reflects (chunks of) linguistic experience,

Pre-packageed information (formulaic expressions) relatively immune from negotiation, which figure prominently in oral discourse, and significantly often coincide with the boundaries of intonational units, where syntactic and pragmatic completion points often converge.(Huang, 1995).

• It takes the functional position (usage-based view) in determining units and patterns (in Chinese), as well as the ontological grounding on the relation between linguistic objects and situations (bits of reality). (Langacker 1987, 1988, 1999; Croft 2002; Tomasello 2003; Bybee 2006, 2010)

• Lexical data at different levels are modularized (only for practical reasons), such as syntax-semantics module, emotion module, discourse and pragmatic module, diachronic module, etc. Researchers from different fields can initiate a new cooperation based upon.

• No more gold standard, please

library(jiebaR)
seg <- worker()
seg["據台大語言所小編謝舒凱表示，宅宅也是非常用功 der"]

##  [1] "據"     "台大"   "語言所" "小編"   "謝舒凱" "表示"   "宅宅"  
##  [8] "也"     "是"     "非常"   "用功"   "der"

txt_khmer <- "តៃវ៉ាន់បោះជំហានឆ្ពោះទៅរកការធ្វើពាណិជ្ជកម្មនៅអាស៊ីដើម្បីកាត់បន្ថយភាពអាស្រ័យលើប្រទេសចិន
"
#Taiwan Steps up Asia Business to Reduce Dependence on China
#taivean baohchomhan chhpaohtow rokkarothveu peanechchokamm now asai daembi 
#katbanthoy pheap asry leu bratesa chen
tokens(txt_khmer)

## tokens from 1 document.
## text1 :
##  [1] "តៃវ៉ាន់"    "បោះជំហាន"  "ឆ្ពោះទៅ"  "រកការធ្វើ" "ពាណិជ្ជកម្ម" "នៅ"      "អាស៊ី"     
##  [8] "ដើម្បី"    "កាត់បន្ថយ"  "ភាព"      "អាស្រ័យ"    "លើ"      "ប្រទេស"   "ចិន"

• Character variants (異體字)
• Fragments
• Non-verbal expressions
• Mixture (e.g., aphabetic words A錢 B咖….)
• Formulaic expressions (incl. Qudraic-syllabic expressions (QIEs))

• sense vector
• pragmatic annotation

DeepLEX-based pilot sudies:

• Affective dialogue system
• Mega- and meta-analysis

• LexicoR: a R packages for Chinese Lexicon(s)

## Loading `database.rds`...

## # A tibble: 3,156 x 300
##    lu_trad lu_simp cld.C1 cld.C123Backwar… cld.C123Backwar…
##    <chr>   <chr>   <chr>             <dbl>            <dbl>
##  1 心      心      心                   NA               NA
##  2 心星    心星    <NA>                 NA               NA
##  3 心淨    心净    <NA>                 NA               NA
##  4 心驚    心惊    心                   NA               NA
##  5 平心    平心    <NA>                 NA               NA
##  6 舒心    舒心    舒                   NA               NA
##  7 心寒    心寒    心                   NA               NA
##  8 丹心    丹心    <NA>                 NA               NA
##  9 心疚    心疚    <NA>                 NA               NA
## 10 淨心    净心    <NA>                 NA               NA
## # … with 3,146 more rows, and 295 more variables:
## #   cld.C123ConditionalProbability <dbl>, cld.C123Entropy <dbl>,
## #   cld.C12BackwardConditionalProbability <dbl>,
## #   cld.C12BackwardEntropy <dbl>, cld.C12ConditionalProbability <dbl>,
## #   cld.C12Entropy <dbl>, cld.C1BackwardConditionalProbability <dbl>,
## #   cld.C1BackwardEntropy <dbl>, cld.C1ConditionalProbability <dbl>,
## #   cld.C1Entropy <dbl>, cld.C1FamilyFrequency <dbl>,
## #   cld.C1FamilySize <int>, cld.C1Frequency <dbl>,
## #   cld.C1FrequencyRaw <int>, cld.C1FrequencyRawSUBTL <int>,
## #   cld.C1FrequencyRawWeibo <int>, cld.C1FrequencySUBTL <dbl>,
## #   cld.C1FrequencyWeibo <dbl>, cld.C1Friends <int>,
## #   cld.C1FriendsFrequency <dbl>, cld.C1HomographsFrequency <dbl>,
## #   cld.C1HomographTokens <int>, cld.C1HomographTypes <int>,
## #   cld.C1HomophonesFrequency <dbl>, cld.C1HomophoneTokens <int>,
## #   cld.C1HomophoneTypes <int>, cld.C1InitialDiphoneFrequency <dbl>,
## #   cld.C1InitialPhonemeFrequency <dbl>, cld.C1IPA <chr>,
## #   cld.C1MaxDiphoneFrequency <dbl>, cld.C1MaxPhonemeFrequency <dbl>,
## #   cld.C1MeanDiphoneFrequency <dbl>, cld.C1MeanPhonemeFrequency <dbl>,
## #   cld.C1MinDiphoneFrequency <dbl>, cld.C1MinPhonemeFrequency <dbl>,
## #   cld.C1OLDPixels <dbl>, cld.C1Phonemes <int>,
## #   cld.C1PhonologicalFrequency <dbl>, cld.C1PhonologicalN <int>,
## #   cld.C1PictureSize <int>, cld.C1Pinyin <chr>, cld.C1Pixels <int>,
## #   cld.C1PLD <dbl>, cld.C1PR <chr>,
## #   cld.C1PRBackwardEnemiesFrequency <dbl>,
## #   cld.C1PRBackwardEnemiesTokens <int>,
## #   cld.C1PRBackwardEnemiesTypes <int>, cld.C1PREnemiesFrequency <dbl>,
## #   cld.C1PREnemiesTokens <int>, cld.C1PREnemiesTypes <int>,
## #   cld.C1PRFamilySize <int>, cld.C1PRFrequency <dbl>,
## #   cld.C1PRFriends <int>, cld.C1PRFriendsFrequency <dbl>,
## #   cld.C1PRPinyin <chr>, cld.C1PRRegularity <int>, cld.C1PRStrokes <int>,
## #   cld.C1RE <dbl>, cld.C1SR <chr>, cld.C1SRFamilySize <int>,
## #   cld.C1SRFrequency <dbl>, cld.C1SRStrokes <int>, cld.C1Strokes <int>,
## #   cld.C1Structure <fct>, cld.C1Tone <fct>, cld.C1Type <fct>,
## #   cld.C2 <chr>, cld.C2FamilyFrequency <dbl>, cld.C2FamilySize <int>,
## #   cld.C2Frequency <dbl>, cld.C2FrequencyRaw <int>,
## #   cld.C2FrequencyRawSUBTL <int>, cld.C2FrequencyRawWeibo <int>,
## #   cld.C2FrequencySUBTL <dbl>, cld.C2FrequencyWeibo <dbl>,
## #   cld.C2Friends <int>, cld.C2FriendsFrequency <dbl>,
## #   cld.C2HomographsFrequency <dbl>, cld.C2HomographTokens <int>,
## #   cld.C2HomographTypes <int>, cld.C2HomophonesFrequency <dbl>,
## #   cld.C2HomophoneTokens <int>, cld.C2HomophoneTypes <int>,
## #   cld.C2InitialDiphoneFrequency <dbl>,
## #   cld.C2InitialPhonemeFrequency <dbl>, cld.C2IPA <chr>,
## #   cld.C2MaxDiphoneFrequency <dbl>, cld.C2MaxPhonemeFrequency <dbl>,
## #   cld.C2MeanDiphoneFrequency <dbl>, cld.C2MeanPhonemeFrequency <dbl>,
## #   cld.C2MinDiphoneFrequency <dbl>, cld.C2MinPhonemeFrequency <dbl>,
## #   cld.C2OLDPixels <dbl>, cld.C2Phonemes <int>,
## #   cld.C2PhonologicalFrequency <dbl>, cld.C2PhonologicalN <int>,
## #   cld.C2PictureSize <int>, cld.C2Pinyin <chr>, cld.C2Pixels <int>,
## #   cld.C2PLD <dbl>, …

• Bybee, J. 1998. The Emergent Lexicon. CLD34: The Panels.

• Huang, S.F. 1995. Emergent Lexical Semantics.

• Hsieh, S.K. et al. 2019. Fluid Annotation: A Granularity-aware Annotation Tool for Chinese Word Fluidity. LREC. 2019.

• Hsieh, Shu-Kai and Yu-Hsiang Tseng. 2019. Linguistic Granularity Annottion Framework: a granularity-aware approach to Chinese NLP. Journal of Granular Computing. Springer. (under review).

• Tseng, Yu-Hiang and Shu-Kai Hsieh. 2019. Eigencharacters.

• 謝舒凱，曾昱翔。2019. 深度詞庫:邁向知識導向的人工智慧基礎。中華心理學刊 61(3).