{"id":32925,"date":"2018-05-04T20:02:11","date_gmt":"2018-05-04T17:02:11","guid":{"rendered":"https:\/\/www.altoros.com\/blog\/?p=32925"},"modified":"2021-12-14T00:17:09","modified_gmt":"2021-12-13T21:17:09","slug":"approaches-and-models-for-applying-natural-language-processing","status":"publish","type":"post","link":"https:\/\/www.altoros.com\/blog\/approaches-and-models-for-applying-natural-language-processing\/","title":{"rendered":"Approaches and Models for Applying Natural Language Processing"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-transparent ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.altoros.com\/blog\/approaches-and-models-for-applying-natural-language-processing\/#Employ_science_to_predict_wisely\" >Employ science to predict wisely<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.altoros.com\/blog\/approaches-and-models-for-applying-natural-language-processing\/#Traditional_approaches\" >Traditional approaches<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.altoros.com\/blog\/approaches-and-models-for-applying-natural-language-processing\/#Adaptive_models_in_real-life_implementations\" >Adaptive models in real-life implementations<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.altoros.com\/blog\/approaches-and-models-for-applying-natural-language-processing\/#Want_details_Watch_the_video\" >Want details? Watch the video!<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.altoros.com\/blog\/approaches-and-models-for-applying-natural-language-processing\/#Further_reading\" >Further reading<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.altoros.com\/blog\/approaches-and-models-for-applying-natural-language-processing\/#About_the_expert\" >About the expert<\/a><\/li><\/ul><\/nav><\/div>\n<h3><span class=\"ez-toc-section\" id=\"Employ_science_to_predict_wisely\"><\/span>Employ science to predict wisely<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>A while ago, the Huffington Post made <a href=\"https:\/\/www.huffpost.com\/entry\/the-hathaway-effect-how-a_b_830041\" rel=\"noopener noreferrer\" target=\"_blank\">a curious observation<\/a> that whenever an Oscar-winning Anne Hathaway hits the headlines, the stock price for Berkshire Hathaway (<a href=\"http:\/\/www.investorguide.com\/stock.php?ticker=BRK-A\" rel=\"noopener noreferrer\" target=\"_blank\">BRK-A<\/a>) goes up. Prior to Academy Awards 2018, the company&#8217;s share gained 2.02%, which amounted to already 2.94% over the weekend after the ceremony. And this is not a one-off, as the same pattern can be tracked down to the opening of &#8220;Love and Other Drugs&#8221; in 2010, when BRK-A went 1.62% up, or the opening of &#8220;Bride Wars&#8221; in 2009 with BRK-A rising by 2.61%.<\/p>\n<p>This may well be an indication of stock market analysts using, let&#8217;s say, <a href=\"https:\/\/arxiv.org\/abs\/1010.3003\" rel=\"noopener noreferrer\" target=\"_blank\">Twitter sentiment analysis<\/a> to predict market behavior. So, this is a good example of applying <a href=\"https:\/\/www.altoros.com\/blog\/how-tensorflow-can-help-to-perform-natural-language-processing-tasks\/\">natural language processing<\/a> to real-world needs at large and to the trading industry in particular.<\/p>\n<p><center><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2018\/05\/pyLDAvis-Interactive-Visualisation-sentiment-analysis-v1.png\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2018\/05\/pyLDAvis-Interactive-Visualisation-sentiment-analysis-v1-1024x582.png\" alt=\"\" width=\"640\" class=\"aligncenter size-large wp-image-32955\" \/><\/a><small>A sample visualization of sentiment analysis (<a href=\"https:\/\/www.clarin.eu\/blog\/blog-post-yin-yin-lu-nlp-meets-k-pop-meertens-instituut\" rel=\"noopener noreferrer\" target=\"_blank\">Image credit<\/a>)<\/small><\/center><\/p>\n<p>At the <a href=\"https:\/\/www.meetup.com\/ru-RU\/TensorFlow-New-York\/events\/249158698\/\" rel=\"noopener noreferrer\" target=\"_blank\">recent TensorFlow meetup<\/a> in New York, <a href=\"https:\/\/www.linkedin.com\/in\/anandvishal94\/\" rel=\"noopener noreferrer\" target=\"_blank\">Vishal Anand<\/a>, a researcher from Columbia University, explored what means of natural language processing are applicable to drive value from market data, make further predictions, and deliver insightful business reports to customers.<\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Traditional_approaches\"><\/span>Traditional approaches<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>There is a variety of natural language processing models to achieve the above-mentioned goals. Traditional approaches to treating data include:<\/p>\n<ul>\n<li style=\"margin-bottom: 6px;\"><a href=\"https:\/\/nlp.stanford.edu\/software\/tokenizer.shtml\" rel=\"noopener noreferrer\" target=\"_blank\">Tokenization<\/a> or sentence splitting, which involves breaking text into a sequence of tokens, roughly corresponding to words.\n<\/li>\n<li style=\"margin-bottom: 6px;\"><a href=\"https:\/\/en.wikipedia.org\/wiki\/Part-of-speech_tagging\" rel=\"noopener noreferrer\" target=\"_blank\">Part-of-speech tagging<\/a> means identifying words as a part of speech they belong to based on both its definition and context.<\/li>\n<li style=\"margin-bottom: 6px;\"><a href=\"https:\/\/www.nlpworld.co.uk\/nlp-glossary\/c\/chunking\/\" rel=\"noopener noreferrer\" target=\"_blank\">Chunking<\/a> or the Hierarchy of Ideas is based on grouping bits of information in order to come to a deductive \/ inductive conclusion.<\/li>\n<li style=\"margin-bottom: 6px;\"><a href=\"https:\/\/en.wikipedia.org\/wiki\/Named-entity_recognition\" rel=\"noopener noreferrer\" target=\"_blank\">Named-entity recognition<\/a> is locating and classifying named entities within text into such pre-defined categories as the names of organization or places.<\/li>\n<li style=\"margin-bottom: 6px;\"><a href=\"https:\/\/nlp.stanford.edu\/projects\/coref.shtml\" rel=\"noopener noreferrer\" target=\"_blank\">Coreference resolution<\/a> involves identifying all the expressions that refer to the very same entity in a text. This can be further used in natural language understanding to perform document summarization, question answering, or information extraction.<\/li>\n<li><a href=\"https:\/\/web.stanford.edu\/~jurafsky\/slp3\/22.pdf\" rel=\"noopener noreferrer\" target=\"_blank\">Semantic role labeling<\/a> is assigning roles to the constituents or phrases in sentences.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Adaptive_models_in_real-life_implementations\"><\/span>Adaptive models in real-life implementations<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>One may also employ a bunch of adaptive or self-learning models that help to improve predictions on ever-changing data.<\/p>\n<p>For example, <a href=\"https:\/\/www.altoros.com\/blog\/text-prediction-with-tensorflow-and-long-short-term-memory-in-six-steps\/\">long short-term memory networks<\/a> (LSTMs) are a good choice to classify, process, and predict time-series data based on time lags of unknown size and duration between important events. This way, real-life applications of LSTMs goes far beyond text recognition or <a href=\"https:\/\/www.altoros.com\/blog\/using-tensorflow-to-compose-music-like-the-one-of-bach-or-the-beatles\/\">composing music<\/a>, but enabling <a href=\"https:\/\/www.altoros.com\/blog\/tensorflow-in-finance-discussing-predictive-analytics-and-budget-planning\/\">predictive analytics<\/a> in finance, as well.<\/p>\n<p>Coupled with the attention mechanism, which helps to identify and focus on the relevant bits of data, LSTMs can achieve better precision, however it comes with additional complexity of computations.<\/p>\n<p><center><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2018\/05\/long-short-term-memory-networks-with-attention-mechanism.png\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2018\/05\/long-short-term-memory-networks-with-attention-mechanism-1024x588.png\" alt=\"\" width=\"640\" class=\"aligncenter size-large wp-image-32950\" \/><\/a><small>LSTMs with the attention mechanism (<a href=\"https:\/\/arxiv.org\/abs\/1704.04368\" rel=\"noopener noreferrer\" target=\"_blank\">Image credit<\/a>)<\/center><\/p>\n<p><a href=\"https:\/\/en.wikipedia.org\/wiki\/Generative_adversarial_network\">Generative adversarial networks<\/a> (GANs) belong to the world of unsupervised machine learning and comprise two neural networks, one of which generates candidates and the other evaluates them. These networks are widely used to generate content of different kind, produce samples of images to visualize interior\/industrial design, model patterns of motion in a video, reconstruct 3D models of objects from images, etc. GANs are also fit for making recommendations based on the input.<\/p>\n<p><center><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2018\/05\/generative-adversarial-network.png\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2018\/05\/generative-adversarial-network.png\" alt=\"\" width=\"640\" class=\"aligncenter size-full wp-image-32951\" \/><\/a><small>A sample architecture of a generative adversarial network (<a href=\"https:\/\/www.kdnuggets.com\/2017\/01\/generative-adversarial-networks-hot-topic-machine-learning.html\" rel=\"noopener noreferrer\" target=\"_blank\">Image credit<\/a>)<\/center><\/p>\n<p>Surely, there are many more other neural networks trained to perform natural language processing tasks in a text, audio, or video to drive meaningful insights and make predictions. When paired with TensorFlow, the variety of <a href=\"https:\/\/www.altoros.com\/blog\/natural-language-processing-and-tensorflow-implementation-across-industries\/\">implementations across industries<\/a> is almost unbelievable.<\/p>\n<p>You can also check out Vishal&#8217;s <a href=\"http:\/\/vishalanand.net\" rel=\"noopener noreferrer\" target=\"_blank\">slides<\/a> from the meetup.<\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Want_details_Watch_the_video\"><\/span>Want details? Watch the video!<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><center class=\"video-display-mobile\"><script src=\"https:\/\/fast.wistia.com\/embed\/medias\/ackq2kxpfm.jsonp\" async><\/script><script src=\"https:\/\/fast.wistia.com\/assets\/external\/E-v1.js\" async><\/script><\/p>\n<div class=\"wistia_embed wistia_async_ackq2kxpfm\" style=\"height:360px;position:relative;width:640px\">\n<div class=\"wistia_swatch\" style=\"height:100%;left:0;opacity:0;overflow:hidden;position:absolute;top:0;transition:opacity 200ms;width:100%;\"><img decoding=\"async\" src=\"https:\/\/fast.wistia.com\/embed\/medias\/ackq2kxpfm\/swatch\" style=\"filter:blur(5px);height:100%;object-fit:contain;width:100%;\" alt=\"\" onload=\"this.parentNode.style.opacity=1;\" \/><\/div>\n<\/div>\n<p><\/center><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Further_reading\"><\/span>Further reading<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li><a href=\"https:\/\/www.altoros.com\/blog\/natural-language-processing-and-tensorflow-implementation-across-industries\/\">Natural Language Processing and TensorFlow Implementation Across Industries<\/a><\/li>\n<li><a href=\"https:\/\/www.altoros.com\/blog\/tensorflow-for-foreign-exchange-market-analyzing-time-series-data\/\">TensorFlow for Foreign Exchange Market: Analyzing Time-Series Data<\/a><\/li>\n<li><a href=\"https:\/\/www.altoros.com\/blog\/tensorflow-in-finance-discussing-predictive-analytics-and-budget-planning\/\">TensorFlow in Finance: Discussing Predictive Analytics and Budget Planning<\/a><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"About_the_expert\"><\/span>About the expert<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2018\/05\/Vishal-Anand-bio.png\" alt=\"\" width=\"150\" height=\"150\" class=\"alignright size-full wp-image-32928\" \/><\/p>\n<p><small><a href=\"https:\/\/www.linkedin.com\/in\/anandvishal94\/\" rel=\"noopener noreferrer\" target=\"_blank\">Vishal Anand<\/a> is an inventor and an experienced data scientist, specializing in machine learning at Columbia University. After obtaining a bachelor degree in computer science from the Indian Institute of Technology Guwahati, he joined Visa Inc. where Vishal dealt with data-driven risk aspects of financial transactions. He was also awarded a travel grant and sponsorship from Visa for a big data program at Stanford. Vishal has two patent filings in the United States Patent and Trademark Office (USPTO) on machine learning approaches in payment transactions algorithms and a trade-secret with Visa Inc.<\/small><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Employ science to predict wisely<\/p>\n<p>A while ago, the Huffington Post made a curious observation that whenever an Oscar-winning Anne Hathaway hits the headlines, the stock price for Berkshire Hathaway (BRK-A) goes up. Prior to Academy Awards 2018, the company&#8217;s share gained 2.02%, which amounted to already 2.94% over the weekend [&#8230;]<\/p>\n","protected":false},"author":3,"featured_media":32957,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[214],"tags":[748,749],"class_list":["post-32925","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tutorials","tag-machine-learning","tag-tensorflow"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Approaches and Models for Applying Natural Language Processing | Altoros<\/title>\n<meta name=\"description\" content=\"Natural language processing is actively used to recognize patterns, make predictions, produce content, reconstruct 3D models, etc.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.altoros.com\/blog\/approaches-and-models-for-applying-natural-language-processing\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Approaches and Models for Applying Natural Language Processing | Altoros\" \/>\n<meta property=\"og:description\" content=\"Employ science to predict wisely A while ago, the Huffington Post made a curious observation that whenever an Oscar-winning Anne Hathaway hits the headlines, the stock price for Berkshire Hathaway (BRK-A) goes up. Prior to Academy Awards 2018, the company&#8217;s share gained 2.02%, which amounted to already 2.94% over the weekend [...]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.altoros.com\/blog\/approaches-and-models-for-applying-natural-language-processing\/\" \/>\n<meta property=\"og:site_name\" content=\"Altoros\" \/>\n<meta property=\"article:published_time\" content=\"2018-05-04T17:02:11+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2021-12-13T21:17:09+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2018\/05\/natural-language-processing-forreal-world-impelementations-v1.gif\" \/>\n\t<meta property=\"og:image:width\" content=\"640\" \/>\n\t<meta property=\"og:image:height\" content=\"437\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/gif\" \/>\n<meta name=\"author\" content=\"Sophia Turol\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" 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