{"id":478093,"date":"2023-08-09T09:27:19","date_gmt":"2023-08-09T09:27:19","guid":{"rendered":""},"modified":"2023-09-05T11:16:02","modified_gmt":"2023-09-05T11:16:02","slug":"named-entity-recognition-ner","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/fr\/wiki\/named-entity-recognition-ner\/","title":{"rendered":"Reconnaissance d&#039;entit\u00e9 nomm\u00e9e (NER)"},"content":{"rendered":"<p>Br\u00e8ves informations sur la reconnaissance d&#039;entit\u00e9s nomm\u00e9es (NER)\u00a0: La reconnaissance d&#039;entit\u00e9s nomm\u00e9es (NER) est un sous-domaine du traitement du langage naturel (NLP) ax\u00e9 sur l&#039;identification et la classification des entit\u00e9s nomm\u00e9es dans le texte. Les entit\u00e9s nomm\u00e9es peuvent \u00eatre des personnes, des organisations, des lieux, des expressions de temps, des quantit\u00e9s, des valeurs mon\u00e9taires, des pourcentages, etc.<\/p>\n<h2>L&#039;histoire de l&#039;origine de la reconnaissance d&#039;entit\u00e9s nomm\u00e9es (NER) et sa premi\u00e8re mention<\/h2>\n<p>La reconnaissance des entit\u00e9s nomm\u00e9es a commenc\u00e9 \u00e0 prendre forme au d\u00e9but des ann\u00e9es 1990. L&#039;un des premiers exemples de NER a eu lieu lors de la sixi\u00e8me conf\u00e9rence sur la compr\u00e9hension des messages (MUC-6) en 1995. \u00c0 partir de ce moment, la recherche dans ce domaine a commenc\u00e9 \u00e0 prosp\u00e9rer, motiv\u00e9e par la n\u00e9cessit\u00e9 de permettre aux ordinateurs de comprendre et d&#039;interpr\u00e9ter le langage humain plus efficacement.<\/p>\n<h2>Informations d\u00e9taill\u00e9es sur la reconnaissance d&#039;entit\u00e9s nomm\u00e9es (NER)\u00a0: \u00e9largir le sujet<\/h2>\n<p>La reconnaissance d&#039;entit\u00e9s nomm\u00e9es (NER) remplit diverses fonctions dans le traitement des langues naturelles. Ses applications s&#039;\u00e9tendent \u00e0 plusieurs domaines tels que la recherche d&#039;informations, la traduction automatique et l&#039;exploration de donn\u00e9es. Le NER se compose de deux parties principales\u00a0:<\/p>\n<ol>\n<li><strong>Identification de l&#039;entit\u00e9<\/strong>: Localiser et classer des \u00e9l\u00e9ments atomiques dans un texte en cat\u00e9gories pr\u00e9d\u00e9finies telles que des noms de personnes, d&#039;organisations, de lieux, etc.<\/li>\n<li><strong>Classement des entit\u00e9s<\/strong>: Classer les entit\u00e9s identifi\u00e9es dans diff\u00e9rentes classes pr\u00e9d\u00e9finies.<\/li>\n<\/ol>\n<p>Le NER peut \u00eatre abord\u00e9 via des syst\u00e8mes bas\u00e9s sur des r\u00e8gles, un apprentissage supervis\u00e9, un apprentissage semi-supervis\u00e9 et un apprentissage non supervis\u00e9.<\/p>\n<h2>La structure interne de la reconnaissance d&#039;entit\u00e9s nomm\u00e9es (NER)\u00a0: comment fonctionne la reconnaissance d&#039;entit\u00e9s nomm\u00e9es (NER)<\/h2>\n<p>La structure interne du NER comporte plusieurs \u00e9tapes :<\/p>\n<ol>\n<li><strong>Tokenisation<\/strong>: D\u00e9composer le texte en mots ou jetons individuels.<\/li>\n<li><strong>Marquage d&#039;une partie du discours<\/strong>: Identifier les cat\u00e9gories grammaticales des jetons.<\/li>\n<li><strong>Analyse<\/strong>: Analyser la structure grammaticale de la phrase.<\/li>\n<li><strong>Identification et classification des entit\u00e9s<\/strong>: Identifier les entit\u00e9s et les classer dans des cat\u00e9gories pr\u00e9d\u00e9finies.<\/li>\n<\/ol>\n<h2>Analyse des principales caract\u00e9ristiques de la reconnaissance d&#039;entit\u00e9s nomm\u00e9es (NER)<\/h2>\n<p>Les principales caract\u00e9ristiques de NER comprennent\u00a0:<\/p>\n<ol>\n<li><strong>Pr\u00e9cision<\/strong>: Capacit\u00e9 \u00e0 identifier et classer correctement les entit\u00e9s.<\/li>\n<li><strong>Vitesse<\/strong>: Le temps n\u00e9cessaire au traitement du texte.<\/li>\n<li><strong>\u00c9volutivit\u00e9<\/strong>: Capacit\u00e9 \u00e0 g\u00e9rer de grands ensembles de donn\u00e9es.<\/li>\n<li><strong>Ind\u00e9pendance linguistique<\/strong>: Possibilit\u00e9 d&#039;\u00eatre utilis\u00e9 dans diff\u00e9rentes langues.<\/li>\n<li><strong>Adaptabilit\u00e9<\/strong>: Peut \u00eatre personnalis\u00e9 pour des domaines ou des industries sp\u00e9cifiques.<\/li>\n<\/ol>\n<h2>Types de reconnaissance d&#039;entit\u00e9s nomm\u00e9es (NER)\u00a0: utilisez des tableaux et des listes<\/h2>\n<p>Les types de NER peuvent \u00eatre class\u00e9s en\u00a0:<\/p>\n<table>\n<thead>\n<tr>\n<th>Taper<\/th>\n<th>Description<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>NER bas\u00e9 sur des r\u00e8gles<\/td>\n<td>Utilise des r\u00e8gles grammaticales pr\u00e9d\u00e9finies<\/td>\n<\/tr>\n<tr>\n<td>NER supervis\u00e9<\/td>\n<td>Utilise des donn\u00e9es \u00e9tiquet\u00e9es pour les mod\u00e8les de formation<\/td>\n<\/tr>\n<tr>\n<td>NER semi-supervis\u00e9<\/td>\n<td>Combine les donn\u00e9es \u00e9tiquet\u00e9es et non \u00e9tiquet\u00e9es<\/td>\n<\/tr>\n<tr>\n<td>NER non supervis\u00e9<\/td>\n<td>Ne n\u00e9cessite pas de donn\u00e9es \u00e9tiquet\u00e9es<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Fa\u00e7ons d&#039;utiliser la reconnaissance d&#039;entit\u00e9s nomm\u00e9es (NER), probl\u00e8mes et leurs solutions li\u00e9es \u00e0 l&#039;utilisation<\/h2>\n<p>Les moyens d&#039;utiliser NER incluent les moteurs de recherche, le support client, les soins de sant\u00e9, etc. Certains probl\u00e8mes et leurs solutions sont\u00a0:<\/p>\n<ul>\n<li><strong>Probl\u00e8me<\/strong>: Manque de donn\u00e9es \u00e9tiquet\u00e9es.<br \/>\n<strong>Solution<\/strong>: Utiliser l&#039;apprentissage semi-supervis\u00e9 ou non supervis\u00e9.<\/li>\n<li><strong>Probl\u00e8me<\/strong>: Contraintes sp\u00e9cifiques \u00e0 la langue.<br \/>\n<strong>Solution<\/strong>: Adaptez le mod\u00e8le au langage ou au domaine sp\u00e9cifique.<\/li>\n<\/ul>\n<h2>Principales caract\u00e9ristiques et autres comparaisons avec des termes similaires<\/h2>\n<table>\n<thead>\n<tr>\n<th>Fonctionnalit\u00e9<\/th>\n<th>NER<\/th>\n<th>Autres t\u00e2ches PNL<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Se concentrer<\/td>\n<td>Entit\u00e9s nomm\u00e9es<\/td>\n<td>Texte g\u00e9n\u00e9ral<\/td>\n<\/tr>\n<tr>\n<td>Complexit\u00e9<\/td>\n<td>Mod\u00e9r\u00e9 \u00e0 \u00e9lev\u00e9<\/td>\n<td>Varie<\/td>\n<\/tr>\n<tr>\n<td>Application<\/td>\n<td>Sp\u00e9cifique<\/td>\n<td>Large<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Perspectives et technologies du futur li\u00e9es \u00e0 la reconnaissance d&#039;entit\u00e9s nomm\u00e9es (NER)<\/h2>\n<p>Les perspectives futures incluent l&#039;int\u00e9gration du NER avec l&#039;apprentissage en profondeur, une adaptabilit\u00e9 accrue \u00e0 diverses langues et des capacit\u00e9s de traitement en temps r\u00e9el.<\/p>\n<h2>Comment les serveurs proxy peuvent \u00eatre utilis\u00e9s ou associ\u00e9s \u00e0 la reconnaissance d&#039;entit\u00e9s nomm\u00e9es (NER)<\/h2>\n<p>Les serveurs proxy comme ceux fournis par OneProxy peuvent \u00eatre utilis\u00e9s pour r\u00e9cup\u00e9rer des donn\u00e9es pour NER. En anonymisant les demandes, ils permettent une collecte efficace et \u00e9thique de donn\u00e9es textuelles pour la formation et la mise en \u0153uvre de mod\u00e8les NER.<\/p>\n<h2>Liens connexes<\/h2>\n<ul>\n<li><a href=\"https:\/\/nlp.stanford.edu\/software\/CRF-NER.shtml\" target=\"_new\" rel=\"noopener nofollow\">Reconnaissance d&#039;entit\u00e9 nomm\u00e9e Stanford NLP<\/a><\/li>\n<li><a href=\"https:\/\/www.nltk.org\/book\/ch07.html\" target=\"_new\" rel=\"noopener nofollow\">Reconnaissance d&#039;entit\u00e9 nomm\u00e9e NLTK<\/a><\/li>\n<li><a href=\"https:\/\/spacy.io\/usage\/linguistic-features#named-entities\" target=\"_new\" rel=\"noopener nofollow\">Reconnaissance d&#039;entit\u00e9 nomm\u00e9e Spacy<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/fr\/\" target=\"_new\" rel=\"noopener\">OneProxy<\/a>: Pour utiliser des serveurs proxy en conjonction avec NER.<\/li>\n<\/ul>","protected":false},"featured_media":468975,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-478093","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Named Entity Recognition (NER): A Comprehensive Overview<\/mark>","faq_items":[{"question":"What is Named Entity Recognition (NER)?","answer":"<p>Named Entity Recognition (NER) is a subfield of Natural Language Processing (NLP) that identifies and classifies named entities in text. These entities can include persons, organizations, locations, expressions of times, quantities, monetary values, percentages, and more.<\/p>"},{"question":"What are the main applications of Named Entity Recognition?","answer":"<p>Named Entity Recognition is used in various domains such as information retrieval, machine translation, data mining, search engines, customer support, and healthcare.<\/p>"},{"question":"How does Named Entity Recognition (NER) work?","answer":"<p>The process of NER involves several stages including tokenization, part-of-speech tagging, parsing, and finally identifying and classifying the entities into predefined categories such as names of persons, organizations, locations, etc.<\/p>"},{"question":"What are the key features of Named Entity Recognition (NER)?","answer":"<p>Key features of NER include accuracy in identifying and classifying entities, speed in processing text, scalability, language independence, and adaptability to specific domains or industries.<\/p>"},{"question":"What types of Named Entity Recognition (NER) exist?","answer":"<p>There are several types of NER, including Rule-Based NER, which utilizes predefined grammatical rules, Supervised NER that uses labeled data for training models, Semi-Supervised NER that combines labeled and unlabeled data, and Unsupervised NER that does not require labeled data.<\/p>"},{"question":"What are some problems with Named Entity Recognition, and how can they be solved?","answer":"<p>Some common problems include a lack of labeled data and language-specific constraints. These can be solved by utilizing semi-supervised or unsupervised learning methods and adapting the model to specific languages or domains.<\/p>"},{"question":"What are the future perspectives and technologies related to Named Entity Recognition (NER)?","answer":"<p>Future perspectives include integration with deep learning, adaptability to various languages, and the development of real-time processing capabilities.<\/p>"},{"question":"How can proxy servers be used with Named Entity Recognition (NER)?","answer":"<p>Proxy servers, such as those provided by OneProxy, can be used to scrape data for NER. They allow for efficient and ethical gathering of text data by anonymizing the requests, facilitating the training and implementation of NER models.<\/p>"},{"question":"Where can I find more information about Named Entity Recognition (NER)?","answer":"<p>You can learn more about NER from resources such as Stanford NLP Named Entity Recognizer, NLTK Named Entity Recognition, Spacy Named Entity Recognition, and OneProxy's website for utilizing proxy servers in conjunction with NER.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/wiki\/478093","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/wiki\/478093\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/media\/468975"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/fr\/wp-json\/wp\/v2\/media?parent=478093"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}