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<!DOCTYPE html>
<html lang="en">
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<title>DSS Capstone Project: Text Prediction</title>
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0;transform-origin:0 0;-webkit-transform:translate3d(0,100%,0) rotateX(-90deg);-moz-transform:translate3d(0,100%,0) rotateX(-90deg);-ms-transform:translate3d(0,100%,0) rotateX(-90deg);transform:translate3d(0,100%,0) rotateX(-90deg)}.reveal.page .slides{-webkit-perspective-origin:0 50%;-moz-perspective-origin:0 50%;-ms-perspective-origin:0 50%;perspective-origin:0 50%;-webkit-perspective:3000px;-moz-perspective:3000px;-ms-perspective:3000px;perspective:3000px}.reveal.page .slides section{padding:30px;min-height:700px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.reveal.page .slides section.past{z-index:12}.reveal.page .slides section:not(.stack):before{content:'';position:absolute;display:block;width:100%;height:100%;left:0;top:0;background:rgba(0,0,0,.1);-webkit-transform:translateZ(-20px);-moz-transform:translateZ(-20px);-ms-transform:translateZ(-20px);-o-transform:translateZ(-20px);transform:translateZ(-20px)}.reveal.page .slides section:not(.stack):after{content:'';position:absolute;display:block;width:90%;height:30px;left:5%;bottom:0;background:0;z-index:1;border-radius:4px;box-shadow:0 95px 25px rgba(0,0,0,.2);-webkit-transform:translateZ(-90px) rotateX(65deg)}.reveal.page .slides>section.stack{padding:0;background:0}.reveal.page .slides>section.past{-webkit-transform-origin:0 0;-moz-transform-origin:0 0;-ms-transform-origin:0 0;transform-origin:0 0;-webkit-transform:translate3d(-40%,0,0) rotateY(-80deg);-moz-transform:translate3d(-40%,0,0) rotateY(-80deg);-ms-transform:translate3d(-40%,0,0) rotateY(-80deg);transform:translate3d(-40%,0,0) rotateY(-80deg)}.reveal.page .slides>section.future{-webkit-transform-origin:100% 0;-moz-transform-origin:100% 0;-ms-transform-origin:100% 0;transform-origin:100% 0;-webkit-transform:translate3d(0,0,0);-moz-transform:translate3d(0,0,0);-ms-transform:translate3d(0,0,0);transform:translate3d(0,0,0)}.reveal.page .slides>section>section.past{-webkit-transform-origin:0 0;-moz-transform-origin:0 0;-ms-transform-origin:0 0;transform-origin:0 0;-webkit-transform:translate3d(0,-40%,0) rotateX(80deg);-moz-transform:translate3d(0,-40%,0) rotateX(80deg);-ms-transform:translate3d(0,-40%,0) rotateX(80deg);transform:translate3d(0,-40%,0) rotateX(80deg)}.reveal.page .slides>section>section.future{-webkit-transform-origin:0 100%;-moz-transform-origin:0 100%;-ms-transform-origin:0 100%;transform-origin:0 100%;-webkit-transform:translate3d(0,0,0);-moz-transform:translate3d(0,0,0);-ms-transform:translate3d(0,0,0);transform:translate3d(0,0,0)}.reveal .slides section[data-transition=fade],.reveal.fade .slides section,.reveal.fade .slides>section>section{-webkit-transform:none;-moz-transform:none;-ms-transform:none;-o-transform:none;transform:none;-webkit-transition:opacity .5s;-moz-transition:opacity .5s;-ms-transition:opacity .5s;-o-transition:opacity .5s;transition:opacity .5s}.reveal.fade.overview .slides section,.reveal.fade.overview 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section:before{display:none!important}.reveal.overview .slides section>section{opacity:1;cursor:pointer}.reveal.overview .slides section:hover{background:rgba(0,0,0,.3)}.reveal.overview .slides section.present{background:rgba(0,0,0,.3)}.reveal.overview .slides>section.stack{padding:0;background:0;overflow:visible}.reveal .pause-overlay{position:absolute;top:0;left:0;width:100%;height:100%;background:#000;visibility:hidden;opacity:0;z-index:100;-webkit-transition:all 1s ease;-moz-transition:all 1s ease;-ms-transition:all 1s ease;-o-transition:all 1s ease;transition:all 1s ease}.reveal.paused .pause-overlay{visibility:visible;opacity:1}.no-transforms{overflow-y:auto}.no-transforms .reveal .slides{position:relative;width:80%;height:auto!important;top:0;left:50%;margin:0;text-align:center}.no-transforms .reveal .controls,.no-transforms .reveal .progress{display:none!important}.no-transforms .reveal .slides section{display:block!important;opacity:1!important;position:relative!important;height:auto;min-height:auto;top:0;left:-50%;margin:70px 0;-webkit-transform:none;-moz-transform:none;-ms-transform:none;-o-transform:none;transform:none}.no-transforms .reveal .slides section section{left:0}.no-transition{-webkit-transition:none;-moz-transition:none;-ms-transition:none;-o-transition:none;transition:none}.reveal .state-background{position:absolute;width:100%;height:100%;background:rgba(0,0,0,0);-webkit-transition:background 800ms ease;-moz-transition:background 800ms ease;-ms-transition:background 800ms ease;-o-transition:background 800ms ease;transition:background 800ms ease}.alert .reveal .state-background{background:rgba(200,50,30,.6)}.soothe .reveal .state-background{background:rgba(50,200,90,.4)}.blackout .reveal .state-background{background:rgba(0,0,0,.6)}.whiteout .reveal .state-background{background:rgba(255,255,255,.6)}.cobalt .reveal .state-background{background:rgba(22,152,213,.6)}.mint .reveal .state-background{background:rgba(22,213,75,.6)}.submerge .reveal .state-background{background:rgba(12,25,77,.6)}.lila .reveal .state-background{background:rgba(180,50,140,.6)}.sunset .reveal .state-background{background:rgba(255,122,0,.6)}.reveal.rtl .slides,.reveal.rtl .slides h1,.reveal.rtl .slides h2,.reveal.rtl .slides h3,.reveal.rtl .slides h4,.reveal.rtl .slides h5,.reveal.rtl .slides h6{direction:rtl;font-family:sans-serif}.reveal.rtl pre,.reveal.rtl code{direction:ltr}.reveal.rtl ol,.reveal.rtl ul{text-align:right}.reveal.rtl .progress span{float:right}.reveal aside.notes{display:none}.zoomed .reveal *,.zoomed .reveal :before,.zoomed .reveal :after{-webkit-transform:none!important;-moz-transform:none!important;-ms-transform:none!important;transform:none!important;-webkit-backface-visibility:visible!important;-moz-backface-visibility:visible!important;-ms-backface-visibility:visible!important;backface-visibility:visible!important}.zoomed .reveal .progress,.zoomed .reveal .controls{opacity:0}.zoomed .reveal .roll span{background:0}.zoomed .reveal .roll span:after{visibility:hidden}
</style>
<style type="text/css" >
/**
* A simple theme for reveal.js presentations, similar
* to the default theme. The accent color is darkblue.
*
* This theme is Copyright (C) 2012 Owen Versteeg, https://github.com/StereotypicalApps. It is MIT licensed.
* reveal.js is Copyright (C) 2011-2012 Hakim El Hattab, http://hakim.se
*/
/*********************************************
* GLOBAL STYLES
*********************************************/
body {
background: white;
background-color: white; }
.reveal {
font-family: "Lato", sans-serif;
font-size: 36px;
font-weight: 200;
letter-spacing: -0.02em;
color: black; }
::selection {
color: white;
background: rgba(0, 0, 0, 0.99);
text-shadow: none; }
/*********************************************
* HEADERS
*********************************************/
.reveal h1,
.reveal h2,
.reveal h3,
.reveal h4,
.reveal h5,
.reveal h6 {
margin: 0 0 20px 0;
color: black;
font-family: "News Cycle", Impact, sans-serif;
line-height: 0.9em;
letter-spacing: 0.02em;
text-transform: none;
text-shadow: none; }
.reveal h1 {
text-shadow: 0px 0px 6px rgba(0, 0, 0, 0.2); }
/*********************************************
* LINKS
*********************************************/
.reveal a:not(.image) {
color: darkblue;
text-decoration: none;
-webkit-transition: color .15s ease;
-moz-transition: color .15s ease;
-ms-transition: color .15s ease;
-o-transition: color .15s ease;
transition: color .15s ease; }
.reveal a:not(.image):hover {
color: #0000f1;
text-shadow: none;
border: none; }
.reveal .roll span:after {
color: #fff;
background: #00003f; }
/*********************************************
* IMAGES
*********************************************/
.reveal section img {
margin: 15px 0px;
background: rgba(255, 255, 255, 0.12);
border: 4px solid black;
box-shadow: 0 0 10px rgba(0, 0, 0, 0.15);
-webkit-transition: all .2s linear;
-moz-transition: all .2s linear;
-ms-transition: all .2s linear;
-o-transition: all .2s linear;
transition: all .2s linear; }
.reveal a:hover img {
background: rgba(255, 255, 255, 0.2);
border-color: darkblue;
box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
/*********************************************
* NAVIGATION CONTROLS
*********************************************/
.reveal .controls div.navigate-left,
.reveal .controls div.navigate-left.enabled {
border-right-color: darkblue; }
.reveal .controls div.navigate-right,
.reveal .controls div.navigate-right.enabled {
border-left-color: darkblue; }
.reveal .controls div.navigate-up,
.reveal .controls div.navigate-up.enabled {
border-bottom-color: darkblue; }
.reveal .controls div.navigate-down,
.reveal .controls div.navigate-down.enabled {
border-top-color: darkblue; }
.reveal .controls div.navigate-left.enabled:hover {
border-right-color: #0000f1; }
.reveal .controls div.navigate-right.enabled:hover {
border-left-color: #0000f1; }
.reveal .controls div.navigate-up.enabled:hover {
border-bottom-color: #0000f1; }
.reveal .controls div.navigate-down.enabled:hover {
border-top-color: #0000f1; }
/*********************************************
* PROGRESS BAR
*********************************************/
.reveal .progress {
background: rgba(0, 0, 0, 0.2); }
.reveal .progress span {
background: darkblue;
-webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
-moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
-ms-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
-o-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
</style>
<style type="text/css">
.reveal h1 {
font-size: 2.5em;
}
.reveal h1,
.reveal h2,
.reveal h3,
.reveal h4,
.reveal h5,
.reveal h6 {
margin-bottom: .6em;
}
.reveal p,
.reveal table {
margin-bottom: 1em;
}
.reveal li {
margin-bottom: .4em;
}
.reveal ul ul,
.reveal ul ol,
.reveal ol ol,
.reveal ol ul {
margin-top: .4em;
}
.reveal .slides {
text-align: left;
}
.reveal small {
font-size: 0.85em;
}
.reveal pre {
margin-top: 0;
max-width: 95%;
border: 1px solid #ccc;
white-space: pre-wrap;
margin-bottom: 1em;
}
.reveal pre code {
display: block; padding: 0.5em;
font-size: 1.6em;
line-height: 1.1em;
background-color: white;
overflow: visible;
max-height: none;
word-wrap: normal;
}
.reveal code {
overflow: visible;
max-height: none;
}
.reveal code.r {
background-color: #F8F8F8;
}
.reveal code.cpp {
background-color: #F8F8F8;
}
.reveal section del {
text-decoration: none;
color: #AAB1BA;
}
.reveal section img {
border: none;
}
.reveal section .fieldError {
margin-bottom: 25px;
}
.reveal section .fieldError span {
color: red;
}
.prompt .reveal .state-background {
background: #C6D7DC;
}
.quiz-multichoice .reveal .state-background {
background: rgba(254,220,179,1);
}
.quiz-multichoice .reveal ul {
list-style-type: none;
margin-bottom: 30px;
}
.quiz-multichoice .reveal li {
margin-top: 15px;
}
.quiz-multichoice .reveal .quizFeedback {
margin-bottom: 30px;
}
.quiz-multichoice .reveal .quizFeedback img {
border: none;
box-shadow: none;
background: transparent;
float: left;
margin-top: -15px;
}
.quiz-multichoice .reveal .quizFeedback span {
font-size: 1.4em;
margin-left: 12px;
}
.section .reveal .state-background {
background: #96A2B6;
}
.section .reveal h1,
.section .reveal h2,
.section .reveal p {
color: white;
margin-top: 50px;
}
.sub-section .reveal .state-background {
background: #E7E8EA
}
.sub-section .reveal h2,
.sub-section .reveal p {
color: #63717B;
margin-top: 50px;
}
.reveal strong {
color: #25679E;
}
.reveal .controls {
right: -20px;
bottom: 5px;
}
.reveal .controls div.navigate-left {
top: 75px;
}
.reveal .controls div.navigate-right {
left: 54px;
top: 75px;
}
.reveal .controls div.navigate-up {
display: none;
}
.reveal .controls div.navigate-down {
display: none;
}
/*********************************************
* NAVIGATION CONTROLS
*********************************************/
.reveal .controls div.navigate-left,
.reveal .controls div.navigate-left.enabled {
border-right-color: #25679E;
}
.reveal .controls div.navigate-right,
.reveal .controls div.navigate-right.enabled {
border-left-color: #25679E;
}
.reveal .controls div.navigate-up,
.reveal .controls div.navigate-up.enabled {
border-bottom-color: #25679E;
}
.reveal .controls div.navigate-down,
.reveal .controls div.navigate-down.enabled {
border-top-color: #25679E;
}
.reveal .controls div.navigate-left.enabled:hover {
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<h1>DSS Capstone Project: Text Prediction</h1><p>Xing Su<br/>April 26, 2015</p>
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<h3>Data</h3>
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<li>For this project, we used the <em>English</em> files from the <strong>HC Corpora</strong> dataset, which contains ~4M lines of scraped text from news articles, Twitter, and blogs</li>
<li><strong><em>Regular Expression</em></strong> was used to clean and tokenize the raw text in the following manner
<ul>
<li>isolated and replaced items such as <code><emoticon></code>, <code><phone></code>, <code><url></code>, <code><email></code>, <code><date></code>, <code><time></code>, <code><num></code>, as well as <code><profanity></code></li>
<li>expanded apostrophes like <code>don't</code>, <code>we'll</code>, <code>there's</code> to <code>don not</code>, <code>we will</code>, <code>there is</code> respectively</li>
<li>splitting text into multiple sentences</li>
<li>removing any extraneous punctuation and spaces</li>
</ul></li>
</ul>
</div>
</section>
<section data-transition="linear" data-transition-speed="default">
<h3>Exploratory Analysis & Data Structure</h3>
<div class="slideContent" >
<ul>
<li>explored with <code>tau</code> and <code>tm</code> packages to tokenize data and build ngrams, however, ultimately decided to <strong><em>build from scratch</em></strong> to improve performance</li>
<li>chose <code>data.table</code> (fast retrieval) to store the 4-gram Markov model
<ul>
<li>built Ngram tables by dividing the tokenized data into groups of ½/¾ words and tabulating their frequency using the <code>table</code> function</li>
<li><code><s></code> and <code></s></code> tags were added to each sentence to mark beginning/end</li>
<li>the end result is stored as <code>data.tables</code> with each column corresponding to a word and <code>N</code> column as the frequency of the word/group of words</li>
</ul></li>
<li>randomly sampled ~10% of all three of the blogs, news, and twitter dataset to use to build the algorithm</li>
<li>experimented with removing stopwords (“a”, “the”, “or”), but lost quite a bit of accuracy and context and <strong><em>decided to keep them</em></strong></li>
<li>vocabulary is limited to around <strong><em>80,000 words</em></strong> and the rest are replaced with the token <code><unk></code> and the model is trained as such</li>
</ul>
</div>
</section>
<section data-transition="linear" data-transition-speed="default">
<h3>Prediction Algorithm & Adjustment</h3>
<div class="slideContent" >
<ul>
<li>the model cleans/tokenizes the input first, and are fed through two algorithms
<ul>
<li><strong>Autocomplete</strong>: take the last group of letters from the input, look up words in the <code>unigram</code> table that start with the letters, and return the result with the highest frequency</li>
<li><strong>Prediction</strong>: look up the last 3 words in <code>fourgram</code> table, last 2 words in the <code>trigram</code>, and last word in <code>bigram</code> table, respectively in that order, for the most likely prediction; if nothing is found, the top word in <code>unigram</code> table is returned</li>
</ul></li>
<li>tried <strong>interpolation</strong> of all four N-gram tables (see model below) using non-linear optimization (<code>nloptr</code> package) but resulted in ~30% increase in computation time and 8% drop in accuracy</li>
</ul>
<p><span style = "font-size: medium;">\[ P_{interpolated} = 0.1155\frac{Count(w_{i−3},w_{i−2},w_{i−1},w_i)}{Count(w_{i−2},w_{i−1},w_i)} + 0.2364\frac{Count(w_{i−2},w_{i−1},w_i)}{Count(w_{i−1},w_i)} + 0.3757\frac{Count(w_{i−1},w_i)}{Count(w_i)} + 0.2724\frac{Count(w_i)}{Total~Words} \]</span></p>
<ul>
<li>tried <strong>Kneser-Ney Smoothing</strong> but resulted in ~10% increase in computation time and approximately the same accuracy</li>
</ul>
</div>
</section>
<section data-transition="linear" data-transition-speed="default">
<h3>Shinyapp & Instructions</h3>
<div class="slideContent" >
<ul>
<li>the final product can be found <a href="https://sxing.shinyapps.io/shinyapp/">here</a>
<ol>
<li>type/paste a phrase onto the line provided (best if less than line width)</li>
<li>autocomplete hints will appear in <strong style = "color: #d16527;">orange</strong> \( \rightarrow \) works best with typing</li>
<li>predicted word will appear in <strong style = "color: #176de3;">blue</strong> \( \rightarrow \) “I” will appear initially as it is the most common way to begin a sentence
<ul>
<li>if <em style = "color: #AAA;">grey</em> text appears after your cursor, you can press the <code>tab</code> or <strong style = "color: #d16527;">\( \rightarrow \)</strong> to autocomplete the word</li>
</ul></li>
<li>there's an option to show the <em style = "color: #000;">Details</em> panel
<ul>
<li>displays tokenized input phrase</li>
<li>includes option to predict multiple words</li>
</ul></li>
</ol></li>
<li><strong style = "color: #666;">Note:</strong> alternative strategies, exploratory code, and experimental analysis can all be found in this <a href="https://github.com/sux13/TextPrediction">repository</a></li>
</ul>
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