{"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/extracting-entities-of-interest-from-1","title":"Extracting Entities of Interest from Comparative Product Reviews","arxiv_id":"2310.20274","date":"2023-10-31","proceeding":"Conference on Information and Knowledge Management (CIKM) 2017 11","authors":["Jatin Arora","Sumit Agrawal","Pawan Goyal","Sayan Pathak"],"abstract":"This paper presents a deep learning based approach to extract product comparison information out of user reviews on various e-commerce websites. Any comparative product review has three major entities of information: the names of the products being compared, the user opinion (predicate) and the feature or aspect under comparison. All these informing entities are dependent on each other and bound by the rules of the language, in the review. We observe that their inter-dependencies can be captured well using LSTMs. We evaluate our system on existing manually labeled datasets and observe out-performance over the existing Semantic Role Labeling (SRL) framework popular for this task.","url_abs":"https://arxiv.org/abs/2310.20274v1","url_pdf":"https://arxiv.org/pdf/2310.20274v1.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":[{"paper_slug":"extracting-entities-of-interest-from-1","repo_url":"https://github.com/jatinarora2702/Review-Information-Extraction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"semantic-role-labeling","task_name":"Semantic Role Labeling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/predicate-detection-on-product-reviews-2017","task":"Predicate Detection","dataset":"Product Reviews 2017","model":"Bidirectional-LSTM","rank_in_archive_order":1,"of":2,"metrics":{"F1 score":"50.4"},"uses_additional_data":true},{"leaderboard":"/sota/predicate-detection-on-product-reviews-2017","task":"Predicate Detection","dataset":"Product Reviews 2017","model":"Semantic Role Labeling (SRL)","rank_in_archive_order":2,"of":2,"metrics":{"F1 score":"6.5"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}