{"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/corpus-level-fine-grained-entity-typing-using","title":"Corpus-level Fine-grained Entity Typing Using Contextual Information","arxiv_id":"1606.07901","date":"2016-06-25","proceeding":"EMNLP 2015 9","authors":["Yadollah Yaghoobzadeh","Hinrich Schütze"],"abstract":"This paper addresses the problem of corpus-level entity typing, i.e.,\ninferring from a large corpus that an entity is a member of a class such as\n\"food\" or \"artist\". The application of entity typing we are interested in is\nknowledge base completion, specifically, to learn which classes an entity is a\nmember of. We propose FIGMENT to tackle this problem. FIGMENT is\nembedding-based and combines (i) a global model that scores based on aggregated\ncontextual information of an entity and (ii) a context model that first scores\nthe individual occurrences of an entity and then aggregates the scores. In our\nevaluation, FIGMENT strongly outperforms an approach to entity typing that\nrelies on relations obtained by an open information extraction system.","url_abs":"http://arxiv.org/abs/1606.07901v1","url_pdf":"http://arxiv.org/pdf/1606.07901v1.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":[],"tasks":[{"task_slug":"entity-typing","task_name":"Entity Typing"},{"task_slug":"knowledge-base-completion","task_name":"Knowledge Base Completion"},{"task_slug":"open-information-extraction","task_name":"Open Information Extraction"}],"methods":[],"datasets_introduced":[{"slug":"figment","name":"Figment","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.07901","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}