{"id":168583,"date":"2023-06-09T15:09:51","date_gmt":"2023-06-09T14:09:51","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=168583"},"modified":"2023-10-30T19:15:11","modified_gmt":"2023-10-30T18:15:11","slug":"natural-language-processing-definition-and-principles","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles","title":{"rendered":"Natural Language Processing (NLP): Definition and Principles"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"168583\" class=\"elementor elementor-168583\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-aead458 e-flex e-con-boxed e-con\" data-id=\"aead458\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5d70250 elementor-widget elementor-widget-heading\" data-id=\"5d70250\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Have you ever wondered how AI personal assistants like Siri or Cortana work? How your spell checker was able to detect syntax errors that you yourself would not have spotted? How does your search engine manage to guess the words you were about to write from the first letters?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-12153cb e-flex e-con-boxed e-con\" data-id=\"12153cb\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c6719f4 elementor-widget elementor-widget-heading\" data-id=\"c6719f4\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What is NLP?\n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-548e098 elementor-widget elementor-widget-text-editor\" data-id=\"548e098\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><span style=\"font-weight: 400;\">NLP for Natural Language Processing is a discipline that focuses on the understanding, manipulation and generation of natural language by machines.\u00a0 Thus, NLP is really at the interface between <\/span><b>computer science and linguistics<\/b><span style=\"font-weight: 400;\">. It is about the ability of the machine to interact directly with humans.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d693d35 e-flex e-con-boxed e-con\" data-id=\"d693d35\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f5d89f3 elementor-widget elementor-widget-heading\" data-id=\"f5d89f3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What problems does NLP address?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-86bb0a2 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"86bb0a2\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"800\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/NLP_1.jpg\" class=\"attachment-large size-large wp-image-168587\" alt=\"NLP_1\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/NLP_1.jpg 1024w, https:\/\/datascientest.com\/en\/files\/2023\/06\/NLP_1-300x300.jpg 300w, https:\/\/datascientest.com\/en\/files\/2023\/06\/NLP_1-150x150.jpg 150w, https:\/\/datascientest.com\/en\/files\/2023\/06\/NLP_1-768x768.jpg 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ee9acd9 elementor-widget elementor-widget-text-editor\" data-id=\"ee9acd9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">NLP is a fairly generic term that covers a very wide range of applications. Here are the most popular applications:<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-83042d9 elementor-widget elementor-widget-heading\" data-id=\"83042d9\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Machine translation<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f808c76 elementor-widget elementor-widget-text-editor\" data-id=\"f808c76\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The development of machine translation algorithms has truly revolutionized the way texts are translated today. Applications, such as <\/span><a href=\"https:\/\/translate.google.com\/translate_t\"><span style=\"font-weight: 400;\">Google Translate<\/span><\/a><span style=\"font-weight: 400;\">, are able to translate entire texts without any human intervention.<\/span><\/p><p><span style=\"font-weight: 400;\">Because natural language is inherently ambiguous and variable, these applications do not rely on word-for-word replacement, but require true <\/span><b>text analysis and modeling<\/b><span style=\"font-weight: 400;\">, known as Statistical Machine Translation.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d389dcd elementor-widget elementor-widget-heading\" data-id=\"d389dcd\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Sentiment analysis<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-92b193f elementor-widget elementor-widget-text-editor\" data-id=\"92b193f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Also known as &#8220;<\/span><b><i>Opinion Mining<\/i><\/b><span style=\"font-weight: 400;\">&#8220;, sentiment analysis involves identifying subjective information in a text to extract the author&#8217;s opinion.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">For example, when a brand launches a new product, it can use the comments collected on social networks to identify the overall positive or negative sentiment shared by customers.<\/span><\/p><p><span style=\"font-weight: 400;\">In general, sentiment analysis is a way to measure the<\/span><b> level of customer satisfaction<\/b><span style=\"font-weight: 400;\"> with the products or services provided by a company or organization. It can even be much more effective than traditional methods such as surveys.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Indeed, if we are often reluctant to spend time answering long questionnaires, a growing part of consumers nowadays frequently share their opinions on social networks. Thus, the search for negative texts and the identification of the main complaints make it possible to <\/span><b>improve products<\/b><span style=\"font-weight: 400;\">, adapt advertising and reduce the level of customer dissatisfaction.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dda94dd elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"dda94dd\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Understand the challenges addressed in NLP<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1b5ce5f elementor-widget elementor-widget-heading\" data-id=\"1b5ce5f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Marketing<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7467c94 elementor-widget elementor-widget-text-editor\" data-id=\"7467c94\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Marketers also use NLP to find people who are likely to make a purchase.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">They rely on the <\/span><b>behavior of Internet users on websites<\/b><span style=\"font-weight: 400;\">, social networks and search engine queries. This type of analysis allows Google to generate a significant profit by offering the right advertisement to the right people. Each time a visitor clicks on an ad, the advertiser pays up to 50 dollars!<\/span><\/p><p><span style=\"font-weight: 400;\">More generally, <\/span><b>NLP methods<\/b><span style=\"font-weight: 400;\"> can be used to build a rich and comprehensive picture of a company&#8217;s existing market, customers, issues, competition, and growth potential for new products and services.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Raw data sources for this analysis include sales logs, surveys and social media&#8230;<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ca63406 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"ca63406\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"507\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/chatbot-g7679806bf_1280-1024x649.jpg\" class=\"attachment-large size-large wp-image-168585\" alt=\"chatbot\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/chatbot-g7679806bf_1280-1024x649.jpg 1024w, https:\/\/datascientest.com\/en\/files\/2023\/06\/chatbot-g7679806bf_1280-300x190.jpg 300w, https:\/\/datascientest.com\/en\/files\/2023\/06\/chatbot-g7679806bf_1280-768x487.jpg 768w, https:\/\/datascientest.com\/en\/files\/2023\/06\/chatbot-g7679806bf_1280.jpg 1280w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f212f0f elementor-widget elementor-widget-heading\" data-id=\"f212f0f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Chatbots<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f64f798 elementor-widget elementor-widget-text-editor\" data-id=\"f64f798\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">NLP methods are at the heart of how today&#8217;s chatbots work. While these systems are not completely perfect, they can now easily <\/span><b>handle standard tasks<\/b><span style=\"font-weight: 400;\"> such as informing customers about products or services, answering their questions, etc. They are <\/span><b>used across multiple channels<\/b><span style=\"font-weight: 400;\">, including the Internet, applications and messaging platforms. The opening of the Facebook Messenger platform to chatbots in 2016 contributed to their development.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-29e881e elementor-widget elementor-widget-heading\" data-id=\"29e881e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Other application areas :<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7999cdc elementor-widget elementor-widget-text-editor\" data-id=\"7999cdc\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Text classification:<\/b><span style=\"font-weight: 400;\"> this consists of assigning a set of predefined categories to a given text. Text classifiers can be used to organize, structure and categorize a set of texts.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Character recognition:<\/b><span style=\"font-weight: 400;\"> This allows to extract key information from receipts, invoices, checks, legal billing documents, etc., based on <\/span><b>character recognition<\/b><span style=\"font-weight: 400;\">.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Automatic correction:<\/b><span style=\"font-weight: 400;\"> Most text editors today have a spell checker that checks the text for spelling errors.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Automatic summarization: <\/b><span style=\"font-weight: 400;\">NLP methods are also used to produce short, precise and fluid summaries of a longer text document.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0a2ef10 e-flex e-con-boxed e-con\" data-id=\"0a2ef10\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-dbe707e elementor-widget elementor-widget-heading\" data-id=\"dbe707e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What are the main methods used in NLP?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-42213e4 elementor-widget elementor-widget-text-editor\" data-id=\"42213e4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Broadly speaking, we can distinguish two aspects that are essential to any NLP problem:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The &#8220;linguistic&#8221; part, which consists in <\/span><b>preprocessing<\/b><span style=\"font-weight: 400;\"> and <\/span><b>transforming<\/b><span style=\"font-weight: 400;\"> the input information into a usable dataset.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The &#8220;machine learning&#8221; or &#8220;data science&#8221; part, which consists in applying <\/span><a href=\"https:\/\/datascientest.com\/en\/machine-learning-what-is-it-and-why-does-it-change-the-world\"><span style=\"font-weight: 400;\">Machine Learning<\/span><\/a><span style=\"font-weight: 400;\"> or <\/span><a href=\"https:\/\/datascientest.com\/en\/all-about-deep-learning\"><span style=\"font-weight: 400;\">Deep Learning<\/span><\/a><span style=\"font-weight: 400;\"> models to this dataset.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bf1ff2d elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"bf1ff2d\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"450\" src=\"https:\/\/datascientest.com\/en\/files\/2020\/07\/www.peakindicators.png\" class=\"attachment-large size-large wp-image-6006\" alt=\"NLP\" srcset=\"https:\/\/datascientest.com\/en\/files\/2020\/07\/www.peakindicators.png 822w, https:\/\/datascientest.com\/en\/files\/2020\/07\/www.peakindicators-300x169.png 300w, https:\/\/datascientest.com\/en\/files\/2020\/07\/www.peakindicators-768x432.png 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a15bbee elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"a15bbee\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Master the different methods in NLP<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fc2c70f elementor-widget elementor-widget-text-editor\" data-id=\"fc2c70f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">In the following, we will discuss these two aspects, briefly describing the main methods and highlighting the main challenges. We will use a classical example: spam detection.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-086647e elementor-widget elementor-widget-heading\" data-id=\"086647e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">The pre-processing phase: from text to data<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cf79790 elementor-widget elementor-widget-text-editor\" data-id=\"cf79790\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Let&#8217;s assume that you want to be able to <\/span><b>determine whether an e-mail is spam or not<\/b><span style=\"font-weight: 400;\">, based on its content alone. To do this, it is essential to transform the raw data (the text of the email) into usable data.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">The main steps include:<\/span><\/p><ul><li><b style=\"background-color: transparent; color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; text-align: var(--text-align);\">Cleaning:<\/b><span style=\"background-color: transparent; color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; text-align: var(--text-align);\"> Varying according to the source of the data, this phase consists of performing tasks such as removing urls, emoji, etc.<\/span><\/li><li aria-level=\"1\"><b>Data normalization:\u00a0<\/b><\/li><\/ul><ul><li style=\"list-style-type: none;\"><ul><li style=\"font-weight: 400;\" aria-level=\"2\"><b>Tokenization<\/b><span style=\"font-weight: 400;\">, or breaking up the text into multiple pieces called tokens. Example: &#8220;You will find attached the document in question&#8221;; &#8220;You&#8221;, &#8220;will find&#8221;, &#8220;attached&#8221;, &#8220;the document&#8221;, &#8220;in question&#8221;.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"2\"><b>Stemming:<\/b><span style=\"font-weight: 400;\"> the same word can be found in different forms depending on the <\/span><b>gender <\/b><span style=\"font-weight: 400;\">(masculine, feminine), <\/span><b>number <\/b><span style=\"font-weight: 400;\">(singular, plural), <\/span><b>person<\/b><span style=\"font-weight: 400;\"> (me, you, them&#8230;), etc. Stemming generally refers to the crude heuristic process of cutting off the end of words in order to keep only the root of the word.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"2\"><b>Lemmatization<\/b><span style=\"font-weight: 400;\">: this consists of performing the same task but using a <\/span><b>fine-grained vocabulary<\/b><span style=\"font-weight: 400;\"> and <\/span><b>word construction analysis<\/b><span style=\"font-weight: 400;\">. Lemmatization allows to remove only the inflexible endings and thus to isolate the canonical form of the word, known as the lemma.\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"2\"><b>Other operations:<\/b><span style=\"font-weight: 400;\"> deleting numbers, punctuation, symbols and stopwords, changing to lowercase.<\/span><\/li><\/ul><\/li><\/ul><p><span style=\"font-weight: 400;\">In order to apply <\/span><b>Machine Learning methods<\/b><span style=\"font-weight: 400;\"> to natural language problems, it is necessary to transform textual data into numerical data.<\/span><\/p><p><span style=\"font-weight: 400;\">There are several approaches but the main ones remains the Ter-Frequency.\u00a0<\/span><span style=\"color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; text-align: var(--text-align);\">This method consists in counting the number of occurrences of tokens present in the corpus for each text. Each text is then represented by a vector of occurrences. This is generally referred to as a <\/span><b style=\"color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; text-align: var(--text-align);\">Bag-Of-Word<\/b><span style=\"color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; text-align: var(--text-align);\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-74fb122 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"74fb122\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"715\" src=\"https:\/\/datascientest.com\/en\/files\/2020\/07\/Capture-d\u2019e\u0301cran-2020-07-19-a\u0300-11.17.46.png\" class=\"attachment-large size-large wp-image-6008\" alt=\"NLP\" srcset=\"https:\/\/datascientest.com\/en\/files\/2020\/07\/Capture-d\u2019e\u0301cran-2020-07-19-a\u0300-11.17.46.png 808w, https:\/\/datascientest.com\/en\/files\/2020\/07\/Capture-d\u2019e\u0301cran-2020-07-19-a\u0300-11.17.46-300x268.png 300w, https:\/\/datascientest.com\/en\/files\/2020\/07\/Capture-d\u2019e\u0301cran-2020-07-19-a\u0300-11.17.46-768x686.png 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d7eea31 elementor-widget elementor-widget-text-editor\" data-id=\"d7eea31\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Nevertheless, this approach has a major drawback: some words are by nature more used than others, which can lead the model to erroneous results.<\/span><\/p><p><b>Term Frequency-Inverse Document Frequency<\/b><span style=\"font-weight: 400;\"> (TF-IDF): this method consists in <\/span><b>counting the number of occurrences<\/b><span style=\"font-weight: 400;\"> of tokens present in the corpus for each text, which is then divided by the total number of occurrences of these same tokens in the whole corpus.<\/span><\/p><p><span style=\"font-weight: 400;\">For the term x present in the document y, we can define its weight by the following relation:<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7c3ae58 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"7c3ae58\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"248\" height=\"56\" src=\"https:\/\/datascientest.com\/en\/files\/2020\/07\/Capture-d\u2019e\u0301cran-2020-07-19-a\u0300-10.53.53.png\" class=\"attachment-large size-large wp-image-6010\" alt=\"NLP formula\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5baaa09 elementor-widget elementor-widget-text-editor\" data-id=\"5baaa09\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Where :<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">t\u0192x,y is the frequency of term x in y ;<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">d\u0192x is the number of documents containing x ;<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">N is the total number of documents.<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">Thus, this approach provides a vector representation for each text that includes vectors of weights rather than occurrences.<\/span><\/p><p><span style=\"font-weight: 400;\">The efficiency of these methods differs according to the application case. However, they have two main limitations:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The richer the <\/span><b>vocabulary of the corpus<\/b><span style=\"font-weight: 400;\">, the larger the size of the<\/span><b> vectors<\/b><span style=\"font-weight: 400;\">, which can represent a problem for the learning models used in the next step.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Word occurrence counting does not allow accounting for their arrangement and thus for the meaning of the sentences.<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">There is another approach that allows to remedy these problems: <\/span><b><i>Word Embedding<\/i><\/b><span style=\"font-weight: 400;\">. It consists in building vectors of fixed size that take into account the context in which the words are found.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Thus, two words present in similar contexts will have <\/span><b>vectors closer<\/b><span style=\"font-weight: 400;\"> (in terms of vector distance). This allows us to capture both semantic, syntactic or thematic similarities of words.<\/span><\/p><p><span style=\"font-weight: 400;\">A more detailed description of this method will be given in the next section.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6b9a0f6 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"6b9a0f6\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Master the vectors in Machine Learning<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5297352 elementor-widget elementor-widget-heading\" data-id=\"5297352\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">The learning phase: from data to model<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0a8fd92 elementor-widget elementor-widget-text-editor\" data-id=\"0a8fd92\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Overall, we can distinguish 3 main NLP approaches: rule-based methods, classical Machine Learning models and Deep Learning models.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Rule-based methods:<\/b><span style=\"font-weight: 400;\"> Rule-based methods are largely based on the development of domain-specific rules (e.g. regular expressions). They can be used to solve simple problems such as <\/span><b>extracting structured data <\/b><span style=\"font-weight: 400;\">from unstructured data (e.g. web pages).<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">In the case of spam detection, this could consist of considering as spam emails, those that contain buzzwords such as &#8220;promotion&#8221;, &#8220;limited offer&#8221;, etc.<\/span><\/p><p><span style=\"font-weight: 400;\">However, these simple methods can be quickly overwhelmed by the complexity of natural language and prove to be inefficient.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Classical Machine Learning Models: <\/b><span style=\"font-weight: 400;\">Classical machine learning approaches can be used to solve more difficult problems. Unlike methods based on predefined rules, they are based on methods that are really about language understanding. They exploit <\/span><b>data obtained from raw texts pre-processed<\/b><span style=\"font-weight: 400;\"> via one of the methods described above, for example. They can also use data related to the length of sentences, the occurrence of specific words, etc. Furthermore, they generally implement a statistical machine learning model such as <\/span><b>Naive Bayes<\/b><span style=\"font-weight: 400;\">, <\/span><b>Logistic Regression<\/b><span style=\"font-weight: 400;\">, etc.<\/span><\/li><\/ul><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Deep Learning Models: <\/b><span style=\"font-weight: 400;\">The use of deep learning models for NLP problems is currently the subject of much research.\u00a0<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c5415bc elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"c5415bc\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"500\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/artificial-intelligence-g1a369c84c_1280-1024x640.jpg\" class=\"attachment-large size-large wp-image-168586\" alt=\"artificial-intelligence\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/artificial-intelligence-g1a369c84c_1280-1024x640.jpg 1024w, https:\/\/datascientest.com\/en\/files\/2023\/06\/artificial-intelligence-g1a369c84c_1280-300x188.jpg 300w, https:\/\/datascientest.com\/en\/files\/2023\/06\/artificial-intelligence-g1a369c84c_1280-768x480.jpg 768w, https:\/\/datascientest.com\/en\/files\/2023\/06\/artificial-intelligence-g1a369c84c_1280.jpg 1280w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-847bd4f elementor-widget elementor-widget-text-editor\" data-id=\"847bd4f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">These models generalize even better than classical learning approaches because they require a less sophisticated text preprocessing phase: neural layers can be seen as automatic feature extractors.<\/span><\/p><p><span style=\"font-weight: 400;\">This makes it possible to build end-to-end models with little <\/span><b>data preprocessing<\/b><span style=\"font-weight: 400;\">. Apart from the feature engineering part, the learning capabilities of <\/span><b>Deep Learning algorithms<\/b><span style=\"font-weight: 400;\"> are generally more powerful than those of <\/span><b>classical Machine Learning<\/b><span style=\"font-weight: 400;\">, which allows obtaining better scores on various complex hard NLP tasks such as translation.\u00a0<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-424688b e-flex e-con-boxed e-con\" data-id=\"424688b\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-350c8e2 elementor-widget elementor-widget-heading\" data-id=\"350c8e2\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What are the possibilities and challenges of NLP?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-87cc6d2 elementor-widget elementor-widget-text-editor\" data-id=\"87cc6d2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The rules that govern the transformation of natural language text into information are not easy for computers to understand. It requires understanding both the words and the way the concepts are related to deliver the desired message. <\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-60f08a6 elementor-widget elementor-widget-heading\" data-id=\"60f08a6\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Ambiguity<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-aaf7da7 elementor-widget elementor-widget-text-editor\" data-id=\"aaf7da7\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">In natural language, words are unique but can have different meanings depending on the context, resulting in lexical, syntactic and semantic ambiguity. To solve this problem, <\/span><b>NLP proposes several methods<\/b><span style=\"font-weight: 400;\">, such as context evaluation. However, understanding the semantic meaning of words in a sentence is still a work in progress.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a7fc4ae elementor-widget elementor-widget-heading\" data-id=\"a7fc4ae\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Synonymy<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-024e049 elementor-widget elementor-widget-text-editor\" data-id=\"024e049\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Another key phenomenon in <\/span><b>natural language<\/b><span style=\"font-weight: 400;\"> is that we can express the same idea with different terms that also depend on the specific context.\u00a0<\/span><\/p><p><em><span style=\"font-weight: 400;\">For example, the terms &#8220;big&#8221; and &#8220;wide&#8221; may be synonymous in describing an object or a building, but they are not interchangeable in all contexts: &#8220;big&#8221; may mean older.<\/span><\/em><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-187a2d9 elementor-widget elementor-widget-heading\" data-id=\"187a2d9\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Correference<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9410efb elementor-widget elementor-widget-text-editor\" data-id=\"9410efb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Correference tasks involve finding all expressions that refer to the same entity. This is an important step for <\/span><b>many high-level NLP tasks<\/b><span style=\"font-weight: 400;\"> that involve whole-text understanding, such as document summarization, question answering, and information extraction. This problem has seen a revival with the introduction of state-of-the-art Deep Learning techniques.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1c6903e elementor-widget elementor-widget-heading\" data-id=\"1c6903e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Writing style<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-12d8c1a elementor-widget elementor-widget-text-editor\" data-id=\"12d8c1a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Depending on the author&#8217;s personality, intentions and emotions, the same idea can be expressed in different ways. Some authors <\/span><b>do not hesitate to use irony or sarcasm<\/b><span style=\"font-weight: 400;\">, and thus convey a meaning opposite to the literal one.<\/span><\/p><p><span style=\"font-weight: 400;\">So while humans can easily master a language, the <\/span><b>ambiguity<\/b><span style=\"font-weight: 400;\"> and <\/span><b>imprecise characteristics<\/b><span style=\"font-weight: 400;\"> of natural languages are what make NLP difficult for machines to implement.<\/span><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5b3280a elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"5b3280a\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Start a training in Machine Learning<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-60a43ff e-flex e-con-boxed e-con\" data-id=\"60a43ff\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f079d9e elementor-widget elementor-widget-html\" data-id=\"f079d9e\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"What is NLP?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"NLP for Natural Language Processing is a discipline that focuses on the understanding, manipulation and generation of natural language by machines.  Thus, NLP is really at the interface between computer science and linguistics. It is about the ability of the machine to interact directly with humans.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"What are the main methods used in NLP?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Broadly speaking, we can distinguish two aspects that are essential to any NLP problem: The \\\"linguistic\\\" part, which consists in preprocessing and transforming the input information into a usable dataset. The \\\"machine learning\\\" or \\\"data science\\\" part, which consists in applying Machine Learning or Deep Learning models to this dataset.\"\r\n    }\r\n  }]\r\n}\r\n<\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Have you ever wondered how AI personal assistants like Siri or Cortana work? How your spell checker was able to detect syntax errors that you yourself would not have spotted? How does your search engine manage to guess the words you were about to write from the first letters? What is NLP? NLP for Natural [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":168588,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Natural Language Processing (NLP): Definition and Principles<\/title>\n<meta name=\"description\" content=\"Have you ever wondered how AI personal assistants like Siri or Cortana work? How your spell checker was able to detect syntax errors that you yourself would not have spotted?\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Natural Language Processing (NLP): Definition and Principles\" \/>\n<meta property=\"og:description\" content=\"Have you ever wondered how AI personal assistants like Siri or Cortana work? How your spell checker was able to detect syntax errors that you yourself\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles\" \/>\n<meta property=\"og:site_name\" content=\"Data Science Courses | DataScientest\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataScientestEN\" \/>\n<meta property=\"article:published_time\" content=\"2023-06-09T14:09:51+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-10-30T18:15:11+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/Natural-Language-Processing.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1025\" \/>\n\t<meta property=\"og:image:height\" content=\"562\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Daniel\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataScientest__\" \/>\n<meta name=\"twitter:site\" content=\"@DataScientest__\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Daniel\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimated reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"9 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles#article\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles\"},\"author\":{\"name\":\"Daniel\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/person\/5a86e30c5090234fb43dd8baa83c44b0\"},\"headline\":\"Natural Language Processing (NLP): Definition and Principles\",\"datePublished\":\"2023-06-09T14:09:51+00:00\",\"dateModified\":\"2023-10-30T18:15:11+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles\"},\"wordCount\":1798,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/datascientest.com\/en\/#organization\"},\"articleSection\":[\"Data Science\"],\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles#respond\"]}],\"copyrightYear\":\"2023\",\"copyrightHolder\":{\"@id\":\"https:\/\/datascientest.com\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles\",\"url\":\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles\",\"name\":\"Natural Language Processing (NLP): Definition and Principles\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/en\/#website\"},\"datePublished\":\"2023-06-09T14:09:51+00:00\",\"dateModified\":\"2023-10-30T18:15:11+00:00\",\"description\":\"Have you ever wondered how AI personal assistants like Siri or Cortana work? How your spell checker was able to detect syntax errors that you yourself would not have spotted?\",\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/datascientest.com\/en\/#website\",\"url\":\"https:\/\/datascientest.com\/en\/\",\"name\":\"Data Science Courses | DataScientest\",\"description\":\"Leader in Data Science Courses\",\"publisher\":{\"@id\":\"https:\/\/datascientest.com\/en\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/datascientest.com\/en\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-GB\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/datascientest.com\/en\/#organization\",\"name\":\"DataScientest\",\"url\":\"https:\/\/datascientest.com\/en\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-GB\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png\",\"contentUrl\":\"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png\",\"width\":301,\"height\":301,\"caption\":\"DataScientest\"},\"image\":{\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/www.facebook.com\/DataScientestEN\",\"https:\/\/twitter.com\/DataScientest__\",\"https:\/\/www.linkedin.com\/school\/datascientest\/mycompany\/\",\"https:\/\/www.tiktok.com\/@datascientest\"]},{\"@type\":\"Person\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/person\/5a86e30c5090234fb43dd8baa83c44b0\",\"name\":\"Daniel\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-GB\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/837acad4f2dc47e85d81cce837261670?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/837acad4f2dc47e85d81cce837261670?s=96&d=mm&r=g\",\"caption\":\"Daniel\"},\"sameAs\":[\"https:\/\/www.linkedin.com\/school\/datascientest\/\",\"user\"],\"jobTitle\":\"ing\u00e9nieur logiciel\",\"worksFor\":\"DataScientest\",\"url\":\"https:\/\/datascientest.com\/en\/author\/team-redac\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Natural Language Processing (NLP): Definition and Principles","description":"Have you ever wondered how AI personal assistants like Siri or Cortana work? How your spell checker was able to detect syntax errors that you yourself would not have spotted?","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles","og_locale":"en_GB","og_type":"article","og_title":"Natural Language Processing (NLP): Definition and Principles","og_description":"Have you ever wondered how AI personal assistants like Siri or Cortana work? How your spell checker was able to detect syntax errors that you yourself","og_url":"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles","og_site_name":"Data Science Courses | DataScientest","article_publisher":"https:\/\/www.facebook.com\/DataScientestEN","article_published_time":"2023-06-09T14:09:51+00:00","article_modified_time":"2023-10-30T18:15:11+00:00","og_image":[{"width":1025,"height":562,"url":"https:\/\/datascientest.com\/en\/files\/2023\/06\/Natural-Language-Processing.jpg","type":"image\/jpeg"}],"author":"Daniel","twitter_card":"summary_large_image","twitter_creator":"@DataScientest__","twitter_site":"@DataScientest__","twitter_misc":{"Written by":"Daniel","Estimated reading time":"9 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles#article","isPartOf":{"@id":"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles"},"author":{"name":"Daniel","@id":"https:\/\/datascientest.com\/en\/#\/schema\/person\/5a86e30c5090234fb43dd8baa83c44b0"},"headline":"Natural Language Processing (NLP): Definition and Principles","datePublished":"2023-06-09T14:09:51+00:00","dateModified":"2023-10-30T18:15:11+00:00","mainEntityOfPage":{"@id":"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles"},"wordCount":1798,"commentCount":0,"publisher":{"@id":"https:\/\/datascientest.com\/en\/#organization"},"articleSection":["Data Science"],"inLanguage":"en-GB","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles#respond"]}],"copyrightYear":"2023","copyrightHolder":{"@id":"https:\/\/datascientest.com\/#organization"}},{"@type":"WebPage","@id":"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles","url":"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles","name":"Natural Language Processing (NLP): Definition and Principles","isPartOf":{"@id":"https:\/\/datascientest.com\/en\/#website"},"datePublished":"2023-06-09T14:09:51+00:00","dateModified":"2023-10-30T18:15:11+00:00","description":"Have you ever wondered how AI personal assistants like Siri or Cortana work? How your spell checker was able to detect syntax errors that you yourself would not have spotted?","inLanguage":"en-GB","potentialAction":[{"@type":"ReadAction","target":["https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles"]}]},{"@type":"WebSite","@id":"https:\/\/datascientest.com\/en\/#website","url":"https:\/\/datascientest.com\/en\/","name":"Data Science Courses | DataScientest","description":"Leader in Data Science Courses","publisher":{"@id":"https:\/\/datascientest.com\/en\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/datascientest.com\/en\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-GB"},{"@type":"Organization","@id":"https:\/\/datascientest.com\/en\/#organization","name":"DataScientest","url":"https:\/\/datascientest.com\/en\/","logo":{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/","url":"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png","contentUrl":"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png","width":301,"height":301,"caption":"DataScientest"},"image":{"@id":"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/DataScientestEN","https:\/\/twitter.com\/DataScientest__","https:\/\/www.linkedin.com\/school\/datascientest\/mycompany\/","https:\/\/www.tiktok.com\/@datascientest"]},{"@type":"Person","@id":"https:\/\/datascientest.com\/en\/#\/schema\/person\/5a86e30c5090234fb43dd8baa83c44b0","name":"Daniel","image":{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/datascientest.com\/en\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/837acad4f2dc47e85d81cce837261670?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/837acad4f2dc47e85d81cce837261670?s=96&d=mm&r=g","caption":"Daniel"},"sameAs":["https:\/\/www.linkedin.com\/school\/datascientest\/","user"],"jobTitle":"ing\u00e9nieur logiciel","worksFor":"DataScientest","url":"https:\/\/datascientest.com\/en\/author\/team-redac"}]}},"_links":{"self":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/posts\/168583"}],"collection":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/users\/45"}],"replies":[{"embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/comments?post=168583"}],"version-history":[{"count":0,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/posts\/168583\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/media\/168588"}],"wp:attachment":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/media?parent=168583"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/categories?post=168583"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/tags?post=168583"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}