{"type":"ScholarlyArticle","title":"Attention Based Document-level Relation Extraction with None Class Ranking Loss","authors":[{"name":"Xiaolong Xu","slug":"xiaolong-xu","given":"Xiaolong","family":"Xu","affiliation":"School of Software , Nanjing University of Information Science & Technology , China; Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services , China"},{"name":"Chenbin Li","slug":"chenbin-li","given":"Chenbin","family":"Li","affiliation":"School of Software , Nanjing University of Information Science & Technology , China"},{"name":"Haolong Xiang","slug":"haolong-xiang","given":"Haolong","family":"Xiang","affiliation":"School of Software , Nanjing University of Information Science & Technology , China; Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services , China"},{"name":"Lianyong Qi","slug":"lianyong-qi","given":"Lianyong","family":"Qi","affiliation":"College of Computer Science and Technology , China University of Petroleum , China"},{"name":"Xuyun Zhang","slug":"xuyun-zhang","given":"Xuyun","family":"Zhang","affiliation":"School of Computing , Macquarie University , Australia"},{"name":"Wanchun Dou","slug":"wanchun-dou","given":"Wanchun","family":"Dou","affiliation":"Department of Computer Science and Technology , Nanjing University , China"}],"abstract":"Through document-level relation extraction (RE), the analysis of the global relation between entities in the text is feasible, and more comprehensive and accurate semantic information can be obtained. In document-level RE, the model needs to infer the implicit relations between two entities in different sentences. To obtain more semantic information, existing methods mainly focus on exploring entity representations. However, they ignore the correlations and indivisibility between relations, entities and contexts. Furthermore, current methods only independently estimate the cases of predefined relations, ignoring the case of \"no relation'', which results in poor prediction. To address the above issues, we propose a document-level RE method based on attention mechanisms, which considers the case of \"no relation''. Specifically, our approach leverages graph attention and multi-head attention networks to capture the correlations and indivisibility among relations, entities, and contexts, respectively. In addition, a novel multi-label loss function that promotes large margins in label confidence scores between each predefined class and the none class is employed to improve the prediction performance. Extensive experiments conducted on benchmarking datasets demonstrate that our proposed method outperforms the state-of-the-art baselines with higher accuracy.","doi":"10.24963/ijcai.2024/726","url":"https://ijcai.org/proceedings/2024/726","pdf":"https://ijcai.org/proceedings/2024/0726.pdf","markdown":"https://ijcai.org/proceedings/2024/726.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/726","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":726,"pages":{"first":6569,"last":6577},"keywords":["Natural Language Processing: NLP: Embeddings","Natural Language Processing: NLP: Information extraction"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2015] Dzmitry Bahdanau, Kyung Hyun Cho, and Yoshua Bengio. 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