{"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/improved-relation-classification-by-deep","title":"Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation","arxiv_id":"1601.03651","date":"2016-01-14","proceeding":"COLING 2016 12","authors":["Yan Xu","Ran Jia","Lili Mou","Ge Li","Yunchuan Chen","Yangyang Lu","Zhi Jin"],"abstract":"Nowadays, neural networks play an important role in the task of relation\nclassification. By designing different neural architectures, researchers have\nimproved the performance to a large extent in comparison with traditional\nmethods. However, existing neural networks for relation classification are\nusually of shallow architectures (e.g., one-layer convolutional neural networks\nor recurrent networks). They may fail to explore the potential representation\nspace in different abstraction levels. In this paper, we propose deep recurrent\nneural networks (DRNNs) for relation classification to tackle this challenge.\nFurther, we propose a data augmentation method by leveraging the directionality\nof relations. We evaluated our DRNNs on the SemEval-2010 Task~8, and achieve an\nF1-score of 86.1%, outperforming previous state-of-the-art recorded results.","url_abs":"http://arxiv.org/abs/1601.03651v2","url_pdf":"http://arxiv.org/pdf/1601.03651v2.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":"classification-1","task_name":"Classification"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-classification","task_name":"Relation Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/relation-classification-on-semeval-2010-task-1","task":"Relation Classification","dataset":"SemEval 2010 Task 8","model":"DRNNs","rank_in_archive_order":2,"of":6,"metrics":{"F1":"86.1"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1601.03651","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}