{"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/grid-tagging-scheme-for-aspect-oriented-fine","title":"Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction","arxiv_id":"2010.04640","date":"2020-10-09","proceeding":"Findings of the Association for Computational Linguistics 2020","authors":["Zhen Wu","Chengcan Ying","Fei Zhao","Zhifang Fan","Xinyu Dai","Rui Xia"],"abstract":"Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims at extracting aspect terms and opinion terms from review in the form of opinion pairs or additionally extracting sentiment polarity of aspect term to form opinion triplet. Because of containing several opinion factors, the complete AFOE task is usually divided into multiple subtasks and achieved in the pipeline. However, pipeline approaches easily suffer from error propagation and inconvenience in real-world scenarios. To this end, we propose a novel tagging scheme, Grid Tagging Scheme (GTS), to address the AFOE task in an end-to-end fashion only with one unified grid tagging task. Additionally, we design an effective inference strategy on GTS to exploit mutual indication between different opinion factors for more accurate extractions. To validate the feasibility and compatibility of GTS, we implement three different GTS models respectively based on CNN, BiLSTM, and BERT, and conduct experiments on the aspect-oriented opinion pair extraction and opinion triplet extraction datasets. Extensive experimental results indicate that GTS models outperform strong baselines significantly and achieve state-of-the-art performance.","url_abs":"https://arxiv.org/abs/2010.04640v2","url_pdf":"https://arxiv.org/pdf/2010.04640v2.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":"grid-tagging-scheme-for-aspect-oriented-fine","repo_url":"https://github.com/NJUNLP/GTS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"grid-tagging-scheme-for-aspect-oriented-fine","repo_url":"https://github.com/NaIwo/GTS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"grid-tagging-scheme-for-aspect-oriented-fine","repo_url":"https://github.com/l294265421/GTS-ASOTE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"aspect-sentiment-triplet-extraction","task_name":"Aspect Sentiment Triplet Extraction"},{"task_slug":"aspect-sentiment-opinion-triplet-extraction","task_name":"Aspect-Sentiment-Opinion Triplet Extraction"},{"task_slug":null,"task_name":"Triplet"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"bilstm","method_name":"BiLSTM"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"gts","method_name":"GTS"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/aspect-sentiment-triplet-extraction-on-aste","task":"Aspect Sentiment Triplet Extraction","dataset":"ASTE-Data-V2","model":"GTS","rank_in_archive_order":8,"of":11,"metrics":{"F1":"68.17"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-sentiment-triplet-extraction-on-res14","task":"Aspect Sentiment Triplet Extraction","dataset":"Res14","model":"GTS-BERT","rank_in_archive_order":1,"of":1,"metrics":{"F1":"70.20"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-sentiment-opinion-triplet-extraction","task":"Aspect-Sentiment-Opinion Triplet Extraction","dataset":"Res14","model":"GTS","rank_in_archive_order":2,"of":2,"metrics":{"Precision":"67.3"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2010.04640","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}