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CapstoneFinalPresentation.Rpres
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Coursera: Data Science
=============================================
A presentation on Capstone Project "PredictNextWord"
By Gyaan GM [May 1, 2016]
Introduction
========================================================
<small> The Coursera Data Science Specialization Capstone Project from Johns Hopkins University (JHU) allows
students to create a usable public data product that can show their skills to potential
employers. For this iteration of the class, JHU partnered with SwiftKey
(http://swiftkey.com/en/) to apply data science in the area of **Natural Language Processing**.
The objective of this project is to build a working predictive text model. The data used in the
model came from a **corpus** called HC Corpora (www.corpora.heliohost.org). A corpus is body of text,
usually containing a large number of sentences. [1]
<small>[1] http://desilinguist.org/pdf/crossroads.pdf</small></small>
Algorithm
========================================================
<small>The algorithm developed to predict the next word in a user-entered text string was based on a
classic **N-gram** model. [2] Using a subset of cleaned data from blogs, twitter, and news
Internet files, **Maximum Likelihood Estimation** (MLE) of unigrams, bigrams, and trigrams were computed.
To improve accuracy, **Jelinek-Mercer smoothing** was used in the algorithm, combining
trigram, bigram, and unigram probabilities. [3] Where interpolation failed,
**part-of-speech tagging** (POST) was employed to provide default predictions by part of
speech. [4] Suggested word completion was based on the unigrams. A profanity filter was also utilized
on all output using Google's bad words list. [5]</small>
<small>[2] http://en.wikipedia.org/wiki/N-gram</small>
<small>[3] http://www.ee.columbia.edu/~stanchen/papers/h015l.final.pdf</small>
<small>[4] http://en.wikipedia.org/wiki/Part-of-speech_tagging</small>
<small>[5] https://badwordslist.googlecode.com/files/badwords.txt</small></small>
Shiny App
========================================================
<small> Then a Shiny (http://shiny.rstudio.com/) app that accepts a
phrase as input, suggests word completion from the unigrams, and predicts the most likely next word based
on the linear interpolation of trigrams, bigrams, and unigrams is developed. The web-based application can be found <a href="https://gkgm.shinyapps.io/PredictNextWord/"> here</a> and the source files for this project can be found <a href="https://github.com/gkgm/Capstone_Milestone_Project"> here</a>. App user interface looks like this </small>
How to use App
========================================================
<small>
<i>The user interface of this application works as follows: </i> <br>
When the text [**1**] is entered, the field with the predicted next word [**2**] refreshes instantaneously and also the whole text input [**3**] gets displayed with suggested completion work as shown below in the diagram.
</small>
