{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/representation-learning-of-entities-and","title":"Representation Learning of Entities and Documents from Knowledge Base Descriptions","arxiv_id":"1806.02960","date":"2018-06-08","proceeding":"COLING 2018 8","authors":["Ikuya Yamada","Hiroyuki Shindo","Yoshiyasu Takefuji"],"abstract":"In this paper, we describe TextEnt, a neural network model that learns\ndistributed representations of entities and documents directly from a knowledge\nbase (KB). Given a document in a KB consisting of words and entity annotations,\nwe train our model to predict the entity that the document describes and map\nthe document and its target entity close to each other in a continuous vector\nspace. Our model is trained using a large number of documents extracted from\nWikipedia. The performance of the proposed model is evaluated using two tasks,\nnamely fine-grained entity typing and multiclass text classification. The\nresults demonstrate that our model achieves state-of-the-art performance on\nboth tasks. The code and the trained representations are made available online\nfor further academic research.","url_abs":"http://arxiv.org/abs/1806.02960v1","url_pdf":"http://arxiv.org/pdf/1806.02960v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"representation-learning-of-entities-and","repo_url":"https://github.com/wikipedia2vec/wikipedia2vec","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"representation-learning-of-entities-and","repo_url":"https://github.com/studio-ousia/textent","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"entity-typing","task_name":"Entity Typing"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/entity-typing-on-freebase-figer","task":"Entity Typing","dataset":"Freebase FIGER","model":"TextEnt-full","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"37.4","BEP":"94.8","Macro F1":"84.2","Micro F1":"85.7","P@1":"93.2"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-20news","task":"Text Classification","dataset":"20NEWS","model":"TextEnt-full","rank_in_archive_order":12,"of":16,"metrics":{"Accuracy":"84.5","F-measure":"83.9"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-r8","task":"Text Classification","dataset":"R8","model":"TextEnt-full","rank_in_archive_order":19,"of":21,"metrics":{"Accuracy":"96.7","F-measure":"91"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.02960","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}