{"id":11330,"date":"2020-10-05T10:11:51","date_gmt":"2020-10-05T09:11:51","guid":{"rendered":"https:\/\/datascientest.com\/?p=11330"},"modified":"2023-11-09T14:11:50","modified_gmt":"2023-11-09T14:11:50","slug":"nlp-word-translation","status":"publish","type":"post","link":"https:\/\/datascientest.com\/nlp-word-translation","title":{"rendered":"NLP- Word translation"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"11330\" class=\"elementor elementor-11330\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-848dec5 e-flex e-con-boxed e-con\" data-id=\"848dec5\" 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-d31fc26 elementor-widget elementor-widget-heading\" data-id=\"d31fc26\" 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\"><p>Aujourd\u2019hui, nous allons vous pr\u00e9senter le 4\u00e8me volet de notre passionnant dossier NLP.<\/p>\n<p>Dans cet article, nous allons voir comment construire un algorithme de traduction sur Python -machine translation en anglais- permettant de traduire tout mot provenant d'une langue source vers une langue cible \u00e0 partir des words embeddings.<\/p><\/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-aa52983 e-flex e-con-boxed e-con\" data-id=\"aa52983\" 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-c404041 elementor-widget elementor-widget-text-editor\" data-id=\"c404041\" 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><strong>Vous avez rat\u00e9 les premiers \u00e9pisodes ? Pas de panique les voici :\u00a0<\/strong><\/p><ol><li><a href=\"https:\/\/datascientest.com\/introduction-au-nlp-natural-language-processing\">Introduction au NLP<\/a><\/li><li><a href=\"https:\/\/datascientest.com\/nlp-word-embedding-word2vec\">Word embedding &#8211; Word2vec<\/a><\/li><li><a href=\"https:\/\/datascientest.com\/nlp-word-translation\">NLP &#8211; Word translation<\/a><ol><li><a href=\"#matriceembedding\">Qu&rsquo;est ce qu&rsquo;une matrice de embeddings ?<\/a><\/li><li><a href=\"#algorithme\">Impl\u00e9mentation d&rsquo;un algorithme de traduction et ses limites<\/a><\/li><\/ol><\/li><li><a href=\"https:\/\/datascientest.com\/nlp-twitter-analyse-de-sentiment\">NLP Twitter &#8211; Analyse des sentiments<\/a><\/li><\/ol>\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-6c0d6ec e-flex e-con-boxed e-con\" data-id=\"6c0d6ec\" 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-a98008e elementor-widget elementor-widget-text-editor\" data-id=\"a98008e\" 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;\">Impl\u00e9menter ce genre d\u2019algorithme peut para\u00eetre, au premier abord, un peu flou. Math\u00e9matiquement, cela revient \u00e0<\/span> <b>trouver la matrice de correspondance (ou matrice d\u2019alignement) W<\/b><span style=\"font-weight: 400;\"> r\u00e9solvant le probl\u00e8me d&rsquo;optimisation suivant :<\/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-71f93be elementor-widget elementor-widget-image\" data-id=\"71f93be\" 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=\"226\" height=\"68\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/Word-translation_8-.png\" class=\"attachment-full size-full wp-image-11356\" alt=\"Word translation\" \/>\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-7282285 elementor-widget elementor-widget-text-editor\" data-id=\"7282285\" 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>O\u00f9 :<\/p><ul><li>O<sub>d<\/sub>(R) est l&rsquo;ensemble des matrices orthogonales (i.e. l&rsquo;ensemble des matrices M \u00e0 coefficients r\u00e9elles, inversibles et tel que \ud835\udc40-<sup>1<\/sup>=\ud835\udc40<sup>T <\/sup>).<\/li><li><span style=\"font-weight: 400;\">X et Y sont respectivement les matrices contenant les embeddings des <\/span><b>mots transparents (<\/b><span style=\"font-weight: 400;\">par exemple, table, television, radio, piano, telephone, etc.<\/span><b>) <\/b><span style=\"font-weight: 400;\">pr\u00e9sents dans la langue source (fran\u00e7ais) et dans la langue cible (anglais). X et Y sont ainsi appel\u00e9es respectivement les matrice d&#8217;embeddings des mots fran\u00e7ais et anglais<\/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-332f65d elementor-widget elementor-widget-text-editor\" data-id=\"332f65d\" 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><b>Les mots transparents vont \u00eatre essentiels \u00e0 la construction de l\u2019algorithme de traduction au sens o\u00f9 ils correspondent \u00e0 des points de rep\u00e8re permettant d\u2019aligner les deux espaces vectoriels d\u2019embeddings apr\u00e8s application de\u00a0 la matrice de d\u2019alignement W. Autrement dit, les mots transparents vont permettre de d\u00e9terminer la transformation g\u00e9om\u00e9trique existante entre les deux sous-espaces vectoriels.<\/b><\/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-aa4a2b1 e-flex e-con-boxed e-con\" data-id=\"aa4a2b1\" 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-e6d1fe9 elementor-widget elementor-widget-menu-anchor\" data-id=\"e6d1fe9\" data-element_type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\nbody.elementor-page .elementor-widget-menu-anchor{margin-bottom:0}<\/style>\t\t<div id=\"matriceembedding\" class=\"elementor-menu-anchor\"><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-701eccb elementor-widget elementor-widget-heading\" data-id=\"701eccb\" 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\">Au fait, qu\u2019est ce qu\u2019une matrice de embeddings ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c246da6 elementor-widget elementor-widget-text-editor\" data-id=\"c246da6\" 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;\">Pour expliquer ce qu\u2019est une matrice de embeddings, prenons les trois phrases suivantes :<\/span><\/p><ul><li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Lorsque l&rsquo;ouvrier est parti.<\/span><\/li><li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Lorsque le p\u00eacheur est parti.<\/span><\/li><li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Lorsque le chien est parti.<\/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-ac82009 elementor-widget elementor-widget-text-editor\" data-id=\"ac82009\" 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>Imaginons que nous ne connaissons pas la <b>signification des mots<\/b> &lsquo;ouvrier&rsquo;, &lsquo;p\u00eacheur&rsquo; et &lsquo;chien&rsquo;, nous sommes toutefois capable de dire que ces <b>trois phrases sont identiques<\/b> \u00e0 l&rsquo;exception d&rsquo;un mot.\u00a0 Les contextes \u00e9tant similaires, nous pourrions d\u00e9duire qu&rsquo;il existe une certaine <b>similitude <\/b>entre &lsquo;ouvrier&rsquo;, &lsquo;p\u00eacheur&rsquo; et &lsquo;chien&rsquo;. En appliquant cette id\u00e9e \u00e0 un corpus entier, nous pourrions d\u00e9finir ainsi les relations entre plusieurs mots. Toutefois, une question subsiste : comment repr\u00e9senter au mieux ces relations ? C&rsquo;est ainsi qu&rsquo;appara\u00eet le concept de matrice d&#8217;embeddings.<\/p><p><span style=\"font-weight: 400;\">Par soucis de simplicit\u00e9, imaginons que chaque mot et sa signification puissent \u00eatre repr\u00e9sent\u00e9es dans un espace abstrait de trois dimensions. <\/span><\/p><blockquote><p><span style=\"font-weight: 400;\">Conceptuellement, cela signifie que tous les mots existent en tant que points singuliers dans un espace 3D et n&rsquo;importe quel mot peut \u00eatre d\u00e9fini de mani\u00e8re unique par sa position dans cet espace d\u00e9crit par trois nombres (x, y, z).<\/span><\/p><\/blockquote><p><span style=\"font-weight: 400;\">Toutefois, en r\u00e9alit\u00e9, le sens d&rsquo;un mot est trop compliqu\u00e9 pour \u00eatre simplement d\u00e9crit dans un espace de trois dimensions et en r\u00e8gle g\u00e9n\u00e9rale, il faut pr\u00e8s de 300 dimensions pour d\u00e9finir compl\u00e8tement un mot et sa signification. <\/span><\/p><blockquote><p><span style=\"font-weight: 400;\">Le vecteur de 300 nombres qui identifie un mot donn\u00e9 est appel\u00e9 l&rsquo;<\/span><b>embedding<\/b><span style=\"font-weight: 400;\"> de ce mot.<\/span><\/p><\/blockquote><p><span style=\"font-weight: 400;\">Finalement, une matrice d&#8217;embeddings n&rsquo;est rien d&rsquo;autre qu&rsquo;une matrice contenant une grande vari\u00e9t\u00e9 de mots et leurs embeddings correspondants. Voici ci-dessous, un exemple de matrice d&#8217;embeddings anglais :<\/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-93d6652 elementor-widget elementor-widget-image\" data-id=\"93d6652\" 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=\"512\" height=\"176\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/Word-translation_3-.png\" class=\"attachment-full size-full wp-image-11361\" alt=\"Word translation\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/Word-translation_3-.png 512w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/Word-translation_3--300x103.png 300w\" sizes=\"(max-width: 512px) 100vw, 512px\" \/>\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\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-91ab655 e-flex e-con-boxed e-con\" data-id=\"91ab655\" 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-7621304 elementor-widget elementor-widget-menu-anchor\" data-id=\"7621304\" data-element_type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div id=\"algorithme\" class=\"elementor-menu-anchor\"><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7e5085b elementor-widget elementor-widget-heading\" data-id=\"7e5085b\" 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\">Impl\u00e9mentation d\u2019un algorithme de traduction et ses limites<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3bf7e6d elementor-widget elementor-widget-text-editor\" data-id=\"3bf7e6d\" 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;\">Nous allons dans cette section impl\u00e9menter un algorithme de traduction et essayer de d\u00e9terminer \u00e0 la fois ses forces et ses faiblesses.<\/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-590cf66 elementor-widget elementor-widget-heading\" data-id=\"590cf66\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\"><u>\u00c9tape 0 :<\/u><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-554d6b6 elementor-widget elementor-widget-text-editor\" data-id=\"554d6b6\" 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;\">R\u00e9cup\u00e9ration de deux matrices d\u2019embeddings U1 et U2 respectivement fran\u00e7aises et anglaises pr\u00e9-entra\u00een\u00e9 sur <\/span><b>Common Crawl<\/b><span style=\"font-weight: 400;\"> et <\/span><b>Wikipedia<\/b><span style=\"font-weight: 400;\"> \u00e0 l&rsquo; aide de <\/span><a href=\"https:\/\/fasttext.cc\/\">fastText<\/a><span style=\"font-weight: 400;\">.<\/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-5232a3d elementor-widget elementor-widget-image\" data-id=\"5232a3d\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"2560\" height=\"2438\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/illustration_vector-02.png\" class=\"attachment-full size-full wp-image-11990\" alt=\"embedding\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Matrice d\u2019embeddings U1 et U2<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-afd52bc elementor-widget elementor-widget-shortcode\" data-id=\"afd52bc\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/garybedoucha\/4ea1873b00971dbda24e58a20773f89d.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b6de113 elementor-widget elementor-widget-heading\" data-id=\"b6de113\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\"><u>\u00c9tape 1 :<\/u><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1742b35 elementor-widget elementor-widget-text-editor\" data-id=\"1742b35\" 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;\">Dans un premier temps, nous devons <\/span><b>d\u00e9finir un dictionnaire bilingue <\/b><span style=\"font-weight: 400;\">(par exemple fran\u00e7ais-anglais) contenant tous les mots transparents contenus dans les deux vocabulaires. Une fois ce dernier obtenu, nous devons<\/span><b> construire les matrices d&#8217;embeddings X et Y <\/b><span style=\"font-weight: 400;\">correspondant respectivement aux matrices fran\u00e7aises et anglaises contenant les embeddings associ\u00e9s aux mots du dictionnaire bilingue pr\u00e9c\u00e9demment d\u00e9fini.<\/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-9be46dd elementor-widget elementor-widget-shortcode\" data-id=\"9be46dd\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/garybedoucha\/e945b8353e682615808ea7d6a4da1a40.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-57c1298 elementor-widget elementor-widget-heading\" data-id=\"57c1298\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\"><u>\u00c9tape 2 :<\/u><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0029f39 elementor-widget elementor-widget-image\" data-id=\"0029f39\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"2560\" height=\"2439\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/illustration_vector-01.png\" class=\"attachment-full size-full wp-image-11991\" alt=\"\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/illustration_vector-01.png 2560w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/illustration_vector-01-300x286.png 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/illustration_vector-01-1024x975.png 1024w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/illustration_vector-01-768x732.png 768w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/illustration_vector-01-1536x1463.png 1536w\" sizes=\"(max-width: 2560px) 100vw, 2560px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Sch\u00e9ma explicatif de l\u2019\u00e9tape 2<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-8f46d58 elementor-widget elementor-widget-text-editor\" data-id=\"8f46d58\" 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<blockquote><p><span style=\"font-weight: 400;\">L&rsquo;objectif de cette deuxi\u00e8me \u00e9tape est de trouver l&rsquo;application W (matrice de correspondance) qui projettera l&rsquo;espace vectoriel de mots source (par exemple le fran\u00e7ais) sur l&rsquo;espace vectoriel de mots cible (par exemple l&rsquo;anglais).\u00a0<\/span><\/p><\/blockquote><p><span style=\"font-weight: 400;\">Evidemment, plus la structure des espaces d&#8217;embeddings sera similaire, plus cette m\u00e9thode sera performante (ce qui est le cas ici avec le fran\u00e7ais et l&rsquo;anglais).<\/span><\/p><p><span style=\"font-weight: 400;\">Comme nous l&rsquo;avons vu en pr\u00e9ambule, W* = argmin || WX &#8211; Y ||\u00a0 sous la contrainte que W soit une matrice orthogonale.<\/span><\/p><p><span style=\"font-weight: 400;\">Nous pouvons prouver en utilisant l&rsquo;orthogonalit\u00e9 et les propri\u00e9t\u00e9s de la trace d&rsquo;une matrice que <\/span><\/p><p style=\"text-align: center;\">W*=UV<sup>T <\/sup>avec U\u03a3V<sup>T<\/sup> = SVD (YX<sup>T<\/sup>)<\/p><p><span style=\"font-weight: 400;\">O\u00f9 <a href=\"https:\/\/fr.wikipedia.org\/wiki\/D\u00e9composition_en_valeurs_singuli\u00e8res\">SVD<\/a>\u00a0 est la d\u00e9composition en valeur singuli\u00e8re d&rsquo;une matrice (g\u00e9n\u00e9ralisation du th\u00e9oreme spectral qui \u00e9nonce que toutes matrices orthogonales peuvent \u00eatre diagonalis\u00e9es par une base orthonorm\u00e9e de vecteurs propres).<\/span><\/p><p><span style=\"font-weight: 400;\">Fort du r\u00e9sultat pr\u00e9c\u00e9dent, trouver W revient simplement \u00e0 d\u00e9composer en valeur singuli\u00e8re la matrice <\/span><span style=\"font-weight: 400;\">\ud835\udc4c\ud835\udc4b<\/span><span style=\"font-weight: 400;\"><sup>T<\/sup><\/span><span style=\"font-weight: 400;\">.<\/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-07328ad elementor-widget elementor-widget-shortcode\" data-id=\"07328ad\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/garybedoucha\/49cc5f5c1d4c7f6f756d3394dff8ba95.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-119805e elementor-widget elementor-widget-text-editor\" data-id=\"119805e\" 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;\">Voici la matrice W obtenue en sortie de code :\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-72d0b3c elementor-widget elementor-widget-image\" data-id=\"72d0b3c\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"504\" height=\"193\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/Word-translation_7-.png\" class=\"attachment-full size-full wp-image-11357\" alt=\"\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/Word-translation_7-.png 504w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/Word-translation_7--300x115.png 300w\" sizes=\"(max-width: 504px) 100vw, 504px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Matrice W obtenue<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-0d36e45 elementor-widget elementor-widget-heading\" data-id=\"0d36e45\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\"><u>\u00c9tape 3 :<\/u><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9bbaf5d elementor-widget elementor-widget-image\" data-id=\"9bbaf5d\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"2560\" height=\"2438\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/illustration_vector-03.png\" class=\"attachment-full size-full wp-image-11989\" alt=\"embedding\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/illustration_vector-03.png 2560w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/illustration_vector-03-300x286.png 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/illustration_vector-03-1024x975.png 1024w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/illustration_vector-03-768x731.png 768w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/illustration_vector-03-1536x1463.png 1536w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/illustration_vector-03-2048x1951.png 2048w\" sizes=\"(max-width: 2560px) 100vw, 2560px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Application de la matrice W \u00e0 l\u2019espace vectoriel d\u2019embeddings des mots fran\u00e7ais<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-ea9a0c9 elementor-widget elementor-widget-text-editor\" data-id=\"ea9a0c9\" 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;\">Une fois la matrice d\u2019alignement W obtenue, nous pouvons d\u00e9sormais trouver la traduction en anglais de n&rsquo;importe quel mot pr\u00e9sent dans le vocabulaire fran\u00e7ais en identifiant les plus proches voisins des mots sources dans l&rsquo;espace vectoriel de mots cible.<\/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-c3c1ced elementor-widget elementor-widget-shortcode\" data-id=\"c3c1ced\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/garybedoucha\/cb81402a8eebebe155a44b2d9f03a389.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d9d6363 elementor-widget elementor-widget-text-editor\" data-id=\"d9d6363\" 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;\">Voici les r\u00e9sultats obtenues (\u00e0 chaque fois, 3 termes anglais les plus proches de la traduction et leur probabilit\u00e9 de correspondance avec le mot \u00e0 traduire) :<\/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-cc863aa elementor-widget elementor-widget-image\" data-id=\"cc863aa\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"274\" height=\"298\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/Word-translation_5-.png\" class=\"attachment-full size-full wp-image-11359\" alt=\"Word translation\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">R\u00e9sultat de l\u2019algorithme de traduction construit<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-9f59f01 elementor-widget elementor-widget-text-editor\" data-id=\"9f59f01\" 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;\">Comme nous pouvons le constater, cet algorithme est assez performant et peut servir de <\/span><b>traducteur de mots fran\u00e7ais-anglais<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Nous pourrions essayer d&rsquo;utiliser ce genre d&rsquo;algorithme pour traduire tout un texte :<\/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-c6955b6 elementor-widget elementor-widget-shortcode\" data-id=\"c6955b6\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/garybedoucha\/1c48d2a19c9023d2f4a6d9bc2b3aaa6c.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4e57219 elementor-widget elementor-widget-text-editor\" data-id=\"4e57219\" 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;\">Voici le r\u00e9sultat obtenu<\/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-b5a4c5c elementor-widget elementor-widget-image\" data-id=\"b5a4c5c\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"824\" height=\"21\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/Word-translation_9.png\" class=\"attachment-full size-full wp-image-11364\" alt=\"Word translation\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/Word-translation_9.png 824w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/Word-translation_9-300x8.png 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2020\/10\/Word-translation_9-768x20.png 768w\" sizes=\"(max-width: 824px) 100vw, 824px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-d07ee46 elementor-widget elementor-widget-text-editor\" data-id=\"d07ee46\" 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;\">Compte tenu de la diff\u00e9rence de structure linguistique entre le fran\u00e7ais et l&rsquo;anglais (par exemple l&rsquo;ordre nom-adjectif en fran\u00e7ais, adjectif-nom en anglais), la traduction mot \u00e0 mot n&rsquo;est pas optimale. De plus, nous pouvons remarquer que notre algorithme de traduction ne fonctionne pas pour les mots tels que : de, en, un, une, etc.<\/span><\/p><p><span style=\"font-weight: 400;\">Comme nous pouvions nous en douter, la traduction mot \u00e0 mot n&rsquo;est pas la m\u00e9thode la plus performante pour traduire un texte. Une des solutions possibles serait d&rsquo;utiliser une approche Seq2seq (ou BERT) dont un prochain article lui sera prochainement consacr\u00e9.<\/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-477a70d elementor-widget elementor-widget-text-editor\" data-id=\"477a70d\" 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;\">Cet article vous a plu ?\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Vous aimeriez aller plus loin en <a href=\"https:\/\/datascientest.com\/deep-learning-definition\">Deep Learning<\/a> ?<\/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-d8e23c7 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"d8e23c7\" 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-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formations-data\">\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\">D\u00e9couvrir nos formations<\/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\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Aujourd\u2019hui, nous allons vous pr\u00e9senter le 4\u00e8me volet de notre passionnant dossier NLP. Dans cet article, nous allons voir comment construire un algorithme de traduction sur Python -machine translation en anglais- permettant de traduire tout mot provenant d&rsquo;une langue source vers une langue cible \u00e0 partir des words embeddings. Vous avez rat\u00e9 les premiers \u00e9pisodes [&hellip;]<\/p>\n","protected":false},"author":78,"featured_media":11385,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2497],"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>NLP- Word translation<\/title>\n<meta name=\"description\" content=\"Utiliser le NLP pour construire un algortihme de traduction automatique ? 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