{"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/improving-information-extraction-by-acquiring","title":"Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning","arxiv_id":"1603.07954","date":"2016-03-25","proceeding":"EMNLP 2016 11","authors":["Karthik Narasimhan","Adam Yala","Regina Barzilay"],"abstract":"Most successful information extraction systems operate with access to a large\ncollection of documents. In this work, we explore the task of acquiring and\nincorporating external evidence to improve extraction accuracy in domains where\nthe amount of training data is scarce. This process entails issuing search\nqueries, extraction from new sources and reconciliation of extracted values,\nwhich are repeated until sufficient evidence is collected. We approach the\nproblem using a reinforcement learning framework where our model learns to\nselect optimal actions based on contextual information. We employ a deep\nQ-network, trained to optimize a reward function that reflects extraction\naccuracy while penalizing extra effort. Our experiments on two databases -- of\nshooting incidents, and food adulteration cases -- demonstrate that our system\nsignificantly outperforms traditional extractors and a competitive\nmeta-classifier baseline.","url_abs":"http://arxiv.org/abs/1603.07954v3","url_pdf":"http://arxiv.org/pdf/1603.07954v3.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":"improving-information-extraction-by-acquiring","repo_url":"https://github.com/karthikncode/DeepRL-InformationExtraction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"torch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1603.07954","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}