{"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/introducing-syntactic-structures-into-target","title":"Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning","arxiv_id":"2010.13378","date":"2020-10-26","proceeding":"EMNLP 2020 11","authors":["Amir Pouran Ben Veyseh","Nasim Nouri","Franck Dernoncourt","Dejing Dou","Thien Huu Nguyen"],"abstract":"Targeted opinion word extraction (TOWE) is a sub-task of aspect based sentiment analysis (ABSA) which aims to find the opinion words for a given aspect-term in a sentence. Despite their success for TOWE, the current deep learning models fail to exploit the syntactic information of the sentences that have been proved to be useful for TOWE in the prior research. In this work, we propose to incorporate the syntactic structures of the sentences into the deep learning models for TOWE, leveraging the syntax-based opinion possibility scores and the syntactic connections between the words. We also introduce a novel regularization technique to improve the performance of the deep learning models based on the representation distinctions between the words in TOWE. The proposed model is extensively analyzed and achieves the state-of-the-art performance on four benchmark datasets.","url_abs":"https://arxiv.org/abs/2010.13378v1","url_pdf":"https://arxiv.org/pdf/2010.13378v1.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-based-sentiment-analysis-1","task_name":"Aspect-Based Sentiment Analysis"},{"task_slug":"aspect-based-sentiment-analysis","task_name":"Aspect-Based Sentiment Analysis (ABSA)"},{"task_slug":"aspect-oriented-opinion-extraction","task_name":"Aspect-oriented  Opinion Extraction"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/aspect-oriented-opinion-extraction-on-semeval","task":"Aspect-oriented  Opinion Extraction","dataset":"SemEval-2014 Task-4","model":"ONG","rank_in_archive_order":3,"of":5,"metrics":{"Laptop 2014 (F1)":"75.77","Restaurant 2014 (F1)":"82.33","Restaurant 2015 (F1)":"78.81","Restaurant 2016 (F1)":"86.01"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2010.13378","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}