TextPrediction/textPrediction-rpubs.html at master · sux13/TextPrediction · GitHub

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<!DOCTYPE html>
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  <title>DSS Capstone Project: Text Prediction</title>

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translate3d(0,-300px,0);transform:translate3d(0,-300px,0) rotateX(70deg) translate3d(0,-300px,0)}.reveal .slides>section>section[data-transition=default].future,.reveal .slides>section>section.future{display:block;opacity:0;-webkit-transform:translate3d(0,300px,0) rotateX(-70deg) translate3d(0,300px,0);-moz-transform:translate3d(0,300px,0) rotateX(-70deg) translate3d(0,300px,0);-ms-transform:translate3d(0,300px,0) rotateX(-70deg) translate3d(0,300px,0);transform:translate3d(0,300px,0) rotateX(-70deg) translate3d(0,300px,0)}.reveal .slides>section[data-transition=concave].past,.reveal.concave .slides>section.past{-webkit-transform:translate3d(-100%,0,0) rotateY(90deg) translate3d(-100%,0,0);-moz-transform:translate3d(-100%,0,0) rotateY(90deg) translate3d(-100%,0,0);-ms-transform:translate3d(-100%,0,0) rotateY(90deg) translate3d(-100%,0,0);transform:translate3d(-100%,0,0) rotateY(90deg) translate3d(-100%,0,0)}.reveal .slides>section[data-transition=concave].future,.reveal.concave 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rotateY(-90deg);-moz-transform:translate3d(-100%,0,0) rotateY(-90deg);-ms-transform:translate3d(-100%,0,0) rotateY(-90deg);transform:translate3d(-100%,0,0) rotateY(-90deg)}.reveal.cube .slides>section.future{-webkit-transform-origin:0 0;-moz-transform-origin:0 0;-ms-transform-origin:0 0;transform-origin:0 0;-webkit-transform:translate3d(100%,0,0) rotateY(90deg);-moz-transform:translate3d(100%,0,0) rotateY(90deg);-ms-transform:translate3d(100%,0,0) rotateY(90deg);transform:translate3d(100%,0,0) rotateY(90deg)}.reveal.cube .slides>section>section.past{-webkit-transform-origin:0 100%;-moz-transform-origin:0 100%;-ms-transform-origin:0 100%;transform-origin:0 100%;-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.cube .slides>section>section.future{-webkit-transform-origin:0 0;-moz-transform-origin:0 0;-ms-transform-origin:0 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;
}

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<h1>DSS Capstone Project: Text Prediction</h1><p>Xing Su<br/>April 26, 2015</p>

<div class="slideContent" >

</div>

</section>
<section data-transition="linear" data-transition-speed="default">
<h3>Data</h3>
<div class="slideContent" >
<ul>
<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>&lt;emoticon&gt;</code>, <code>&lt;phone&gt;</code>, <code>&lt;url&gt;</code>, <code>&lt;email&gt;</code>, <code>&lt;date&gt;</code>, <code>&lt;time&gt;</code>, <code>&lt;num&gt;</code>, as well as <code>&lt;profanity&gt;</code></li>
<li>expanded apostrophes like <code>don&#39;t</code>, <code>we&#39;ll</code>, <code>there&#39;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>

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<section data-transition="linear" data-transition-speed="default">
<h3>Exploratory Analysis &amp; 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 &frac12;/&frac34; words and tabulating their frequency using the <code>table</code> function</li>
<li><code>&lt;s&gt;</code> and <code>&lt;/s&gt;</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 (&ldquo;a&rdquo;, &ldquo;the&rdquo;, &ldquo;or&rdquo;), 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>&lt;unk&gt;</code> and the model is trained as such</li>
</ul>

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<section data-transition="linear" data-transition-speed="default">
<h3>Prediction Algorithm &amp; 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 &amp; 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> \( \rightarrow \) &ldquo;I&rdquo; 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&#39;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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