{"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/question-answering-on-freebase-via-relation","title":"Question Answering on Freebase via Relation Extraction and Textual Evidence","arxiv_id":"1603.00957","date":"2016-03-03","proceeding":"ACL 2016 8","authors":["Kun Xu","Siva Reddy","Yansong Feng","Songfang Huang","Dongyan Zhao"],"abstract":"Existing knowledge-based question answering systems often rely on small\nannotated training data. While shallow methods like relation extraction are\nrobust to data scarcity, they are less expressive than the deep meaning\nrepresentation methods like semantic parsing, thereby failing at answering\nquestions involving multiple constraints. Here we alleviate this problem by\nempowering a relation extraction method with additional evidence from\nWikipedia. We first present a neural network based relation extractor to\nretrieve the candidate answers from Freebase, and then infer over Wikipedia to\nvalidate these answers. Experiments on the WebQuestions question answering\ndataset show that our method achieves an F_1 of 53.3%, a substantial\nimprovement over the state-of-the-art.","url_abs":"http://arxiv.org/abs/1603.00957v3","url_pdf":"http://arxiv.org/pdf/1603.00957v3.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":"question-answering-on-freebase-via-relation","repo_url":"https://github.com/syxu828/QuestionAnsweringOverFB","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"semantic-parsing","task_name":"Semantic Parsing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1603.00957","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}