{"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/rsgt-relational-structure-guided-temporal","title":"RSGT: Relational Structure Guided Temporal Relation Extraction","arxiv_id":null,"date":"2022-10-01","proceeding":"COLING 2022 10","authors":["Jie zhou","Shenpo Dong","Hongkui Tu","Xiaodong Wang","Yong Dou"],"abstract":"Temporal relation extraction aims to extract temporal relations between event pairs, which is crucial for natural language understanding. Few efforts have been devoted to capturing the global features. In this paper, we propose RSGT: Relational Structure Guided Temporal Relation Extraction to extract the relational structure features that can fit for both inter-sentence and intra-sentence relations. Specifically, we construct a syntactic-and-semantic-based graph to extract relational structures. Then we present a graph neural network based model to learn the representation of this graph. After that, an auxiliary temporal neighbor prediction task is used to fine-tune the encoder to get more comprehensive node representations. Finally, we apply a conflict detection and correction algorithm to adjust the wrongly predicted labels. Experiments on two well-known datasets, MATRES and TB-Dense, demonstrate the superiority of our method (2.3% F1 improvement on MATRES, 3.5% F1 improvement on TB-Dense).","url_abs":"https://aclanthology.org/2022.coling-1.174","url_pdf":"https://aclanthology.org/2022.coling-1.174.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":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"natural-language-understanding","task_name":"Natural Language Understanding"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"temporal-relation-classification","task_name":"Temporal Relation Classification"},{"task_slug":"temporal-relation-extraction","task_name":"Temporal Relation Extraction"}],"methods":[{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/temporal-relation-classification-on-matres","task":"Temporal Relation Classification","dataset":"MATRES","model":"RSGT","rank_in_archive_order":1,"of":4,"metrics":{"F1":"84.0"},"uses_additional_data":false},{"leaderboard":"/sota/temporal-relation-classification-on-tb-dense","task":"Temporal Relation Classification","dataset":"TB-Dense","model":"RSGT","rank_in_archive_order":2,"of":3,"metrics":{"F1":"68.7"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}