{"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/a-question-answering-approach-to-emotion","title":"A Question Answering Approach to Emotion Cause Extraction","arxiv_id":"1708.05482","date":"2017-08-18","proceeding":null,"authors":["Lin Gui","Jiannan Hu","Yulan He","Ruifeng Xu","Qin Lu","Jiachen Du"],"abstract":"Emotion cause extraction aims to identify the reasons behind a certain\nemotion expressed in text. It is a much more difficult task compared to emotion\nclassification. Inspired by recent advances in using deep memory networks for\nquestion answering (QA), we propose a new approach which considers emotion\ncause identification as a reading comprehension task in QA. Inspired by\nconvolutional neural networks, we propose a new mechanism to store relevant\ncontext in different memory slots to model context information. Our proposed\napproach can extract both word level sequence features and lexical features.\nPerformance evaluation shows that our method achieves the state-of-the-art\nperformance on a recently released emotion cause dataset, outperforming a\nnumber of competitive baselines by at least 3.01% in F-measure.","url_abs":"http://arxiv.org/abs/1708.05482v2","url_pdf":"http://arxiv.org/pdf/1708.05482v2.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":"emotion-cause-extraction","task_name":"Emotion Cause Extraction"},{"task_slug":"emotion-classification","task_name":"Emotion Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/emotion-cause-extraction-on-ece","task":"Emotion Cause Extraction","dataset":"ECE","model":"ConvMS-Memnet","rank_in_archive_order":8,"of":8,"metrics":{"F1":"69.55"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.05482","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}