{"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/combining-neural-networks-and-log-linear","title":"Combining Neural Networks and Log-linear Models to Improve Relation Extraction","arxiv_id":"1511.05926","date":"2015-11-18","proceeding":null,"authors":["Thien Huu Nguyen","Ralph Grishman"],"abstract":"The last decade has witnessed the success of the traditional feature-based\nmethod on exploiting the discrete structures such as words or lexical patterns\nto extract relations from text. Recently, convolutional and recurrent neural\nnetworks has provided very effective mechanisms to capture the hidden\nstructures within sentences via continuous representations, thereby\nsignificantly advancing the performance of relation extraction. The advantage\nof convolutional neural networks is their capacity to generalize the\nconsecutive k-grams in the sentences while recurrent neural networks are\neffective to encode long ranges of sentence context. This paper proposes to\ncombine the traditional feature-based method, the convolutional and recurrent\nneural networks to simultaneously benefit from their advantages. Our systematic\nevaluation of different network architectures and combination methods\ndemonstrates the effectiveness of this approach and results in the\nstate-of-the-art performance on the ACE 2005 and SemEval dataset.","url_abs":"http://arxiv.org/abs/1511.05926v1","url_pdf":"http://arxiv.org/pdf/1511.05926v1.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":null,"task_name":"Relation"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/relation-extraction-on-ace-2005","task":"Relation Extraction","dataset":"ACE 2005","model":"RNN+CNN","rank_in_archive_order":25,"of":30,"metrics":{"Cross Sentence":"No","Relation classification F1":"67.7"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}