{"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/don-t-eclipse-your-arts-due-to-small","title":"Don't Eclipse Your Arts Due to Small Discrepancies: Boundary Repositioning with a Pointer Network for Aspect Extraction","arxiv_id":null,"date":"2020-07-01","proceeding":"ACL 2020 6","authors":["Zhenkai Wei","Yu Hong","Bowei Zou","Meng Cheng","Jianmin Yao"],"abstract":"The current aspect extraction methods suffer from boundary errors. In general, these errors lead to a relatively minor difference between the extracted aspects and the ground-truth. However, they hurt the performance severely. In this paper, we propose to utilize a pointer network for repositioning the boundaries. Recycling mechanism is used, which enables the training data to be collected without manual intervention. We conduct the experiments on the benchmark datasets SE14 of laptop and SE14-16 of restaurant. Experimental results show that our method achieves substantial improvements over the baseline, and outperforms state-of-the-art methods.","url_abs":"https://aclanthology.org/2020.acl-main.339","url_pdf":"https://aclanthology.org/2020.acl-main.339.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":"aspect-extraction","task_name":"Aspect Extraction"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"pointer-net","method_name":"Pointer Network"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/aspect-extraction-on-semeval-2015-task-12","task":"Aspect Extraction","dataset":"SemEval 2015 Task 12","model":"Wei et al. (2020)","rank_in_archive_order":2,"of":2,"metrics":{"Restaurant (F1)":"72.7"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-extraction-on-semeval-2014-task-4-sub-1","task":"Aspect Extraction","dataset":"SemEval-2014 Task-4","model":"Wei et al. (2020)","rank_in_archive_order":5,"of":6,"metrics":{"Laptop (F1)":"82.7","Restaurant (F1)":"87.1"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}