Determining the Next Word from an Input Phrase

• Problem: Given a string of words (i.e., a phrase), predict the next word that is most likely to come next. A solution to this problem would be useful in a vast array of applications such as:
  • type ahead for texting or typing apps
  • assistant apps for individuals with speech disabilities
  • educational applications
• Objective: Using a provided training data set, train a model to predict the next word that would most likely come after a given input n-gram of words (the input phrase). Provide end-users a Shiny app that allows them to type in the input phrase, invokes the model and then presents the output predicted word.

• Filter data
  • Remove “non-word” characters (special characters, numerals, etc.)
  • Remove non-words, i.e., do not include words not in a dictionary
• Consider only 1-, 2-, 3- and 4-grams
• Analyze Data and Train Model Accordingly
  • Divide corpora of data into arrays of various n-grams
  • Create probablity table of given certain n-gram what next word is in corporat P(word|n-gram)
• Use a variant of the Backoff Algorithm
• For more information, see:
  • https://cs.stanford.edu/people/eroberts/courses/soco/projects/2004-05/nlp/techniques_word.html
  • https://en.wikipedia.org/wiki/Katz%27s_back-off_model

summary(cars)

     speed           dist       
 Min.   : 4.0   Min.   :  2.00  
 1st Qu.:12.0   1st Qu.: 26.00  
 Median :15.0   Median : 36.00  
 Mean   :15.4   Mean   : 42.98  
 3rd Qu.:19.0   3rd Qu.: 56.00  
 Max.   :25.0   Max.   :120.00