{"id":3499,"date":"2019-12-01T14:29:00","date_gmt":"2019-12-01T14:29:00","guid":{"rendered":"https:\/\/staging.novelis.io\/?post_type=scientific-pub&#038;p=3499"},"modified":"2022-02-25T10:56:49","modified_gmt":"2022-02-25T10:56:49","slug":"text2sqlnet-syntax-type-aware-tree-networks-pour-la-conversion-de-texte-en-sql","status":"publish","type":"scientific-pub","link":"https:\/\/novelis.io\/fr\/scientific-pub\/text2sqlnet-syntax-type-aware-tree-networks-pour-la-conversion-de-texte-en-sql\/","title":{"rendered":"Text2SQLNet: Syntax Type-Aware Tree Networks pour la conversion de texte en SQL"},"content":{"rendered":"\n<p>D\u00e9couvrez notre article de conf\u00e9rence&nbsp;<a href=\"text2sqlnet: Syntax Type-Aware Tree Networks for Text-to-SQL\">Text2SQLNet : Syntax Type-Aware Tree Networks for Text-to-SQL<\/a>&nbsp;\u2013 Conf\u00e9rence internationale Europe Moyen-Orient &amp; Afrique du Nord Syst\u00e8mes d\u2019information et technologies pour soutenir l\u2019apprentissage \u2013 &nbsp;<a href=\"https:\/\/link.springer.com\/bookseries\/16172\" target=\"_blank\" rel=\"noopener\">Learning and Analytics in Intelligent Systems<\/a>, publi\u00e9 dans Springer Link. Cette \u00e9tude est en anglais.<\/p>\n\n\n\n<p>Merci \u00e0 l\u2019<a href=\"https:\/\/novelis.io\/fr\/laboratoire-rd\/\">\u00e9quipe de recherche de Novelis<\/a> pour son savoir-faire et son expertise.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"resume\">R\u00e9sum\u00e9<\/h2>\n\n\n\n<p>La construction d\u2019interfaces en langage naturel avec des bases de donn\u00e9es relationnelles est un challenge important dans le domaine du traitement du langage naturel (NLP). Elle n\u00e9cessite un syst\u00e8me capable de comprendre les questions en langage naturel et de g\u00e9n\u00e9rer les requ\u00eates SQL correspondantes. Dans cet article, nous pr\u00e9sentons notre id\u00e9e d\u2019utiliser les informations de type et le contenu de la base de donn\u00e9es pour mieux comprendre les entit\u00e9s rares et les nombres dans les questions en langage naturel, afin d\u2019am\u00e9liorer le mod\u00e8le SyntaxSQLNet qui est l\u2019\u00e9tat de l\u2019art dans la t\u00e2che Text-to-SQL. Nous pr\u00e9sentons \u00e9galement l\u2019architecture globale et les techniques qui peuvent \u00eatre utilis\u00e9es dans la mise en \u0153uvre de notre mod\u00e8le de r\u00e9seau neuronal (NN) Text2SQLNet, avec l\u2019int\u00e9gration de notre id\u00e9e qui consiste \u00e0 utiliser les informations sur les types pour mieux comprendre les entit\u00e9s rares et les nombres dans les questions en langage naturel. Nous pouvons \u00e9galement utiliser le contenu de la base de donn\u00e9es pour mieux comprendre la requ\u00eate de l\u2019utilisateur si elle n\u2019est pas bien form\u00e9e. L\u2019impl\u00e9mentation de cette id\u00e9e peut encore am\u00e9liorer les performances de la t\u00e2che Text-to-SQL.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"a-propos-de-l-etude\">A propos de l\u2019\u00e9tude<\/h2>\n\n\n\n<p>\u201cRelational databases store a vast amount of today\u2019s information and provide the foundation of applications such as medical records (Hillestad et al., 2005)[1], financial markets (Beck and al., 2000)[2], and customer relations management (Ngai et al., 2009)[3]. However, accessing relational databases requires an understanding of query languages such as SQL, which, while powerful, is difficult to master. Natural language interfaces (NLI), a research area at the intersection of natural language processing and human- computer interactions, seeks to provide means for humans to interact with computers through the use of natural language (Androutsopoulos et al., 1995)[4]. Natural language always contains ambiguities, each user can express himself in his own way.\u201d<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"lire-l-article-complet\"><a href=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-030-36778-7_48\" target=\"_blank\" rel=\"noopener\">Lire l\u2019article complet<\/a><\/h3>\n\n\n\n<p><a href=\"https:\/\/link.springer.com\/bookseries\/16172\" target=\"_blank\" rel=\"noopener\">Learning and Analytics in Intelligent Systems<\/a>&nbsp;(LAIS, volume 7)<\/p>\n\n\n\n<p><strong>SpringerLink fournit aux chercheurs l\u2019acc\u00e8s \u00e0 des millions de documents scientifiques provenant de revues, livres, s\u00e9ries, protocoles et ouvrages de r\u00e9f\u00e9rence<\/strong>.<\/p>\n","protected":false},"featured_media":2296,"template":"","categories":[24],"custom_tag":[167],"class_list":["post-3499","scientific-pub","type-scientific-pub","status-publish","has-post-thumbnail","hentry","category-publication-scientifique","custom_tag-sql-fr"],"acf":{"summary":"","filter_opacity":"70","subtitle":"","reading_time":"","authors":"","document_to_download":{"upload_a_file":false,"download_without_form":false,"file":false,"url":""},"externel_link":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Text2SQLNet: Syntax Type-Aware Tree Networks pour la conversion de texte en SQL<\/title>\n<meta name=\"description\" content=\"D\u00e9couvrez notre article de conf\u00e9rence\u00a0Text2SQLNet : Syntax Type-Aware Tree Networks for Text-to-SQL\u00a0\u2013 publi\u00e9 dans Springer Link.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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