{"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/going-out-on-a-limb-joint-extraction-of","title":"Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees","arxiv_id":null,"date":"2017-07-01","proceeding":"ACL 2017 7","authors":["Arzoo Katiyar","Claire Cardie"],"abstract":"We present a novel attention-based recurrent neural network for joint extraction of entity mentions and relations. We show that attention along with long short term memory (LSTM) network can extract semantic relations between entity mentions without having access to dependency trees. Experiments on Automatic Content Extraction (ACE) corpora show that our model significantly outperforms feature-based joint model by Li and Ji (2014). We also compare our model with an end-to-end tree-based LSTM model (SPTree) by Miwa and Bansal (2016) and show that our model performs within 1{\\%} on entity mentions and 2{\\%} on relations. Our fine-grained analysis also shows that our model performs significantly better on Agent-Artifact relations, while SPTree performs better on Physical and Part-Whole relations.","url_abs":"https://aclanthology.org/P17-1085","url_pdf":"https://aclanthology.org/P17-1085.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":"relation-extraction","task_name":"Relation Extraction"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/relation-extraction-on-ace-2004","task":"Relation Extraction","dataset":"ACE 2004","model":"Attention","rank_in_archive_order":9,"of":11,"metrics":{"Cross Sentence":"No","NER Micro F1":"79.6","RE+ Micro F1":"45.7"},"uses_additional_data":false},{"leaderboard":"/sota/relation-extraction-on-ace-2005","task":"Relation Extraction","dataset":"ACE 2005","model":"Attention","rank_in_archive_order":12,"of":30,"metrics":{"Cross Sentence":"No","NER Micro F1":"82.6","RE Micro F1":"55.9","RE+ Micro F1":"53.6","Sentence Encoder":"biLSTM"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}