Yangsheng Xu, Jiaxin Tian, Mingwei Tang, Linping Tao, Liuxuan Wang
{"title":"带有实体的文档级关系提取引起了人们的高度关注","authors":"Yangsheng Xu, Jiaxin Tian, Mingwei Tang, Linping Tao, Liuxuan Wang","doi":"10.1016/j.csl.2023.101574","DOIUrl":null,"url":null,"abstract":"<div><p><span><span><span>Document-level Relation Extraction(DocRE) aims to extract relations between entities from documents. In contrast to sentence-level relation extraction, it requires extracting semantic relations from multiple sentences. It is necessary to further improve the performance of the above algorithm in order to extract document-level relation. Therefore, the DocRE algorithms have to deal with more complex entity structure relationships and the need to unite </span>semantic relationships between different sentences when reasoning about relationships between entities. The proposed algorithms fail to infer relationships between entities when dealing with complex entity structure relationships. In this paper, we propose an entity mentions deep attention framework that efficiently infers entity relationships through entity structure and contextual information. Firstly, a structural </span>dependency module of entities is designed to achieve interaction between different mentions of the entity. Secondly, a deep contextual attention component proposed to enrich the </span>semantic information between entities by entity-related contexts. Finally, we use a distance mapping component to solve the problem of entity pairs that are far away from each other. According to our implementation results, our model outperforms the state-ofthe-art models on three public datasets DocRED, DGA, and CDR.</p></div>","PeriodicalId":50638,"journal":{"name":"Computer Speech and Language","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Document-level relation extraction with entity mentions deep attention\",\"authors\":\"Yangsheng Xu, Jiaxin Tian, Mingwei Tang, Linping Tao, Liuxuan Wang\",\"doi\":\"10.1016/j.csl.2023.101574\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span><span><span>Document-level Relation Extraction(DocRE) aims to extract relations between entities from documents. In contrast to sentence-level relation extraction, it requires extracting semantic relations from multiple sentences. It is necessary to further improve the performance of the above algorithm in order to extract document-level relation. Therefore, the DocRE algorithms have to deal with more complex entity structure relationships and the need to unite </span>semantic relationships between different sentences when reasoning about relationships between entities. The proposed algorithms fail to infer relationships between entities when dealing with complex entity structure relationships. In this paper, we propose an entity mentions deep attention framework that efficiently infers entity relationships through entity structure and contextual information. Firstly, a structural </span>dependency module of entities is designed to achieve interaction between different mentions of the entity. Secondly, a deep contextual attention component proposed to enrich the </span>semantic information between entities by entity-related contexts. Finally, we use a distance mapping component to solve the problem of entity pairs that are far away from each other. According to our implementation results, our model outperforms the state-ofthe-art models on three public datasets DocRED, DGA, and CDR.</p></div>\",\"PeriodicalId\":50638,\"journal\":{\"name\":\"Computer Speech and Language\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2023-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Speech and Language\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0885230823000931\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Speech and Language","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0885230823000931","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Document-level relation extraction with entity mentions deep attention
Document-level Relation Extraction(DocRE) aims to extract relations between entities from documents. In contrast to sentence-level relation extraction, it requires extracting semantic relations from multiple sentences. It is necessary to further improve the performance of the above algorithm in order to extract document-level relation. Therefore, the DocRE algorithms have to deal with more complex entity structure relationships and the need to unite semantic relationships between different sentences when reasoning about relationships between entities. The proposed algorithms fail to infer relationships between entities when dealing with complex entity structure relationships. In this paper, we propose an entity mentions deep attention framework that efficiently infers entity relationships through entity structure and contextual information. Firstly, a structural dependency module of entities is designed to achieve interaction between different mentions of the entity. Secondly, a deep contextual attention component proposed to enrich the semantic information between entities by entity-related contexts. Finally, we use a distance mapping component to solve the problem of entity pairs that are far away from each other. According to our implementation results, our model outperforms the state-ofthe-art models on three public datasets DocRED, DGA, and CDR.
期刊介绍:
Computer Speech & Language publishes reports of original research related to the recognition, understanding, production, coding and mining of speech and language.
The speech and language sciences have a long history, but it is only relatively recently that large-scale implementation of and experimentation with complex models of speech and language processing has become feasible. Such research is often carried out somewhat separately by practitioners of artificial intelligence, computer science, electronic engineering, information retrieval, linguistics, phonetics, or psychology.