{"id":18366841,"url":"https://github.com/zamgi/lingvo--ner-en","last_synced_at":"2025-04-06T16:32:26.121Z","repository":{"id":121682391,"uuid":"79555280","full_name":"zamgi/lingvo--Ner-en","owner":"zamgi","description":"Named entity recognition (NER) in English texts","archived":false,"fork":false,"pushed_at":"2023-11-25T16:31:47.000Z","size":15579,"stargazers_count":8,"open_issues_count":1,"forks_count":1,"subscribers_count":4,"default_branch":"master","last_synced_at":"2023-11-25T17:36:51.327Z","etag":null,"topics":["linguistics","lingvo","named-entity-recognition","natural-language-processing","ner","nlp","nlp-machine-learning"],"latest_commit_sha":null,"homepage":"","language":"C#","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/zamgi.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2017-01-20T11:56:40.000Z","updated_at":"2023-06-12T16:19:33.000Z","dependencies_parsed_at":"2023-11-22T23:34:27.386Z","dependency_job_id":null,"html_url":"https://github.com/zamgi/lingvo--Ner-en","commit_stats":null,"previous_names":[],"tags_count":0,"template":null,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zamgi%2Flingvo--Ner-en","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zamgi%2Flingvo--Ner-en/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zamgi%2Flingvo--Ner-en/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zamgi%2Flingvo--Ner-en/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/zamgi","download_url":"https://codeload.github.com/zamgi/lingvo--Ner-en/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":223257151,"owners_count":17114835,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["linguistics","lingvo","named-entity-recognition","natural-language-processing","ner","nlp","nlp-machine-learning"],"created_at":"2024-11-05T23:19:10.218Z","updated_at":"2024-11-05T23:19:10.816Z","avatar_url":"https://github.com/zamgi.png","language":"C#","funding_links":[],"categories":[],"sub_categories":[],"readme":"# lingvo--Ner-en\n\n\u003ca target=\"_blank\" href=\"http://ner-en.apphb.com/index.html\"\u003e[ live demo ]\u003c/a\u003e\n\n\u003cdiv style=\"padding: 20px\"\u003e\n    \u003cp\u003e\n        Named entity recognition (NER)\u003cbr /\u003e        \n    \u003c/p\u003e\n    \u003cdiv\u003e\n        \u003cspan\u003eThis system is designed to determine the type of proper names, event names, products, place names and so forth.\u003c/span\u003e\n        \u003cbr /\u003e\n        \u003cspan\u003eThe classification into three types:\u003c/span\u003e\n        \u003cbr /\u003e\n        \u003cul style=\"margin-left: 30px\"\u003e\n            \u003cli\u003e1. Individuals (name or any component name, for example, \u003cspan class=\"B_NAME\" title=\"Individuals\"\u003eTed Bundy\u003c/span\u003e)\u003c/li\u003e\n            \u003cli\u003e2. The legal entity (company names, companies, parties and the like, for example, \u003cspan class=\"B_ORG\" title=\"Entities\"\u003eUPS Law School\u003c/span\u003e, \u003cspan class=\"B_ORG\" title=\"Entities\"\u003eCentral Washington University\u003c/span\u003e)\u003c/li\u003e\n            \u003cli\u003e3. Geographical names (cities, states, and the like, for example, \u003cspan class=\"B_GEO\" title=\"Geographic features\"\u003eVermont\u003c/span\u003e, \u003cspan class=\"B_GEO\" title=\"Geographic features\"\u003eWashington\u003c/span\u003e)\u003c/li\u003e\n        \u003c/ul\u003e\n        \u003cbr /\u003e\n        \u003cspan\u003eNamed types are not defined by the dictionary, and based on statistical algorithms.\u003c/span\u003e\n        \u003cbr /\u003e\n        \u003cspan\u003eNumber and description of types of classes given at the stage of training (getting statistical model).\u003c/span\u003e\n        \u003cbr /\u003e\n        \u003cspan\u003eThe accuracy of the types of entities (as F1):\u003c/span\u003e\n        \u003cul style=\"margin-left: 30px\"\u003e\n            \u003cli\u003e1. individuals - about 94% \u003c/li\u003e\n            \u003cli\u003e2. legal entities - about 91% \u003c/li\u003e\n            \u003cli\u003e3. geographical names - 95% \u003c/li\u003e\n        \u003c/ul\u003e\n        \u003cbr /\u003e\n        \u003cspan\u003eThis system works with the english text and classifies words containing uppercase letter.\u003c/span\u003e\n    \u003c/div\u003e\n\u003c/div\u003e\n\u003chr /\u003e\n\u003cdiv style=\"padding: 20px; padding-top: 5px;\"\u003e\n    \u003cp\u003e\n        Автоматическое определение именованных сущностей (NER - Named-Entity Recognition)\u003cbr /\u003e        \n    \u003c/p\u003e\n    \u003cdiv\u003e\n        \u003cspan\u003eДанная система предназначен для определения типа имен собственных, названий событий, продуктов, топонимов и пр.\u003c/span\u003e\n        \u003cbr /\u003e\n        \u003cspan\u003eКлассификация производится на три типа:\u003c/span\u003e\n        \u003cbr /\u003e\n        \u003cul style=\"margin-left: 30px\"\u003e\n            \u003cli\u003e1. физические лица (ФИО или любая составляющая ФИО, например, \u003cspan class=\"B_NAME\" title=\"Физ. лица\"\u003eTed Bundy\u003c/span\u003e)\u003c/li\u003e\n            \u003cli\u003e2. юридически лица (названия компаний, обществ, партий и т.п., например, \u003cspan class=\"B_ORG\" title=\"Юр. лица\"\u003eUPS Law School\u003c/span\u003e, \u003cspan class=\"B_ORG\" title=\"Юр. лица\"\u003eCentral Washington University\u003c/span\u003e)\u003c/li\u003e\n            \u003cli\u003e3. географические названия (города, штаты и т.п., например, \u003cspan class=\"B_GEO\" title=\"Географические объекты\"\u003eVermont\u003c/span\u003e, \u003cspan class=\"B_GEO\" title=\"Географические объекты\"\u003eWashington\u003c/span\u003e)\u003c/li\u003e\n        \u003c/ul\u003e\n        \u003cbr /\u003e\n        \u003cspan\u003eИменованные типы определяются не словарем, а на основе статистических алгоритмов.\u003c/span\u003e\n        \u003cbr /\u003e\n        \u003cspan\u003eКоличество типов и описание их классов задается на этапе обучения (получения статистической модели).\u003c/span\u003e\n        \u003cbr /\u003e\n        \u003cspan\u003eТочность определения типов сущностей (по мере F1):\u003c/span\u003e\n        \u003cul style=\"margin-left: 30px\"\u003e\n            \u003cli\u003e1. физические лица - около 94% \u003c/li\u003e\n            \u003cli\u003e2. юридически лица - около 91% \u003c/li\u003e\n            \u003cli\u003e3. географические названия - 95% \u003c/li\u003e\n        \u003c/ul\u003e\n        \u003cbr /\u003e\n        \u003cspan\u003eДанная система работает с англоязычными текстами и классифицирует слова, содержащие заглавную букву.\u003c/span\u003e\n    \u003c/div\u003e\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzamgi%2Flingvo--ner-en","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzamgi%2Flingvo--ner-en","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzamgi%2Flingvo--ner-en/lists"}