Coursera Data Science Capstone: Course Project

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• Predicts next word as the user types a sentence
• Similar to the way most smart phone keyboards are implemented today using the technology of Swiftkey

• A subset of the original data was sampled from the three sources (blogs,twitter and news) which is then merged into one.
• Next, data cleaning is done by conversion to lowercase, strip white space, and removing punctuation and numbers.
• The corresponding n-grams are then created (Quadgram,Trigram and Bigram).
• Next, the term-count tables are extracted from the N-Grams and sorted according to the frequency in descending order.
• Lastly, the n-gram objects are saved as R-Compressed files (.RData files).

• N-gram model with “Stupid Backoff” (Brants et al 2007)
• Checks if highest-order (in this case, n=4) n-gram has been seen. If not “degrades” to a lower-order model (n=3, 2); we would use even higher orders, but ShinyApps caps app size at 100mb

• The code is available on GitHub
• Further work can expand the main weakness of this approach: long-range context
  1. Current algorithm discards contextual information past 4-grams
  2. We can incorporate this into future work through clustering underlying training corpus/data and predicting what cluster the entire sentence would fall into
  3. This allows us to predict using ONLY the data subset that fits the long-range context of the sentence, while still preserving the performance characteristics of an n-gram and Stupid Backoff model