{"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-with-subgraph-embeddings","title":"Question Answering with Subgraph Embeddings","arxiv_id":"1406.3676","date":"2014-06-14","proceeding":"EMNLP 2014 10","authors":["Antoine Bordes","Sumit Chopra","Jason Weston"],"abstract":"This paper presents a system which learns to answer questions on a broad\nrange of topics from a knowledge base using few hand-crafted features. Our\nmodel learns low-dimensional embeddings of words and knowledge base\nconstituents; these representations are used to score natural language\nquestions against candidate answers. Training our system using pairs of\nquestions and structured representations of their answers, and pairs of\nquestion paraphrases, yields competitive results on a competitive benchmark of\nthe literature.","url_abs":"http://arxiv.org/abs/1406.3676v3","url_pdf":"http://arxiv.org/pdf/1406.3676v3.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-with-subgraph-embeddings","repo_url":"https://github.com/gmtt/CSCI590","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"question-answering","task_name":"Question Answering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-answering-on-webquestions","task":"Question Answering","dataset":"WebQuestions","model":"Subgraph embeddings","rank_in_archive_order":36,"of":37,"metrics":{"F1":"39.2%"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1406.3676","atlas_url":"https://app.syntology.ai/?focus=1406.3676","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}