Wenfei Hu , Lu Liu , Yupeng Sun , Yu Wu , Zhicheng Liu , Ruixin Zhang , Tao Peng
{"title":"NLIRE: A Natural Language Inference method for Relation Extraction","authors":"Wenfei Hu , Lu Liu , Yupeng Sun , Yu Wu , Zhicheng Liu , Ruixin Zhang , Tao Peng","doi":"10.1016/j.websem.2021.100686","DOIUrl":null,"url":null,"abstract":"<div><p>Relation extraction task aims at detecting the semantic relation between a pair of entities in a given target sentence. However, previous methods lack the description of the relation definition, thus needing to model the implication of relations during training. To tackle this issue, we propose a natural language inference method for relation extraction. Given a premise and a hypothesis, the natural language inference task refers to predicting whether the facts in the premise necessarily imply the facts in the hypothesis. Specifically, for each relation type, we construct a relation description. These relation descriptions are the definition of relation, containing prior knowledge that helps model understand the meaning of relation. The given target sentence is viewed as the premise, and these descriptions are viewed as the hypotheses. Then model infers whether these hypotheses can be concluded from the premise. Based on the inference results, our model selects the relation corresponding to the most confident hypothesis as the prediction. Substantial experiments on SemEval2010 Task8 dataset demonstrate that the proposed method achieves state-of-the-art performance.</p></div>","PeriodicalId":49951,"journal":{"name":"Journal of Web Semantics","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Web Semantics","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1570826821000561","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 3
Abstract
Relation extraction task aims at detecting the semantic relation between a pair of entities in a given target sentence. However, previous methods lack the description of the relation definition, thus needing to model the implication of relations during training. To tackle this issue, we propose a natural language inference method for relation extraction. Given a premise and a hypothesis, the natural language inference task refers to predicting whether the facts in the premise necessarily imply the facts in the hypothesis. Specifically, for each relation type, we construct a relation description. These relation descriptions are the definition of relation, containing prior knowledge that helps model understand the meaning of relation. The given target sentence is viewed as the premise, and these descriptions are viewed as the hypotheses. Then model infers whether these hypotheses can be concluded from the premise. Based on the inference results, our model selects the relation corresponding to the most confident hypothesis as the prediction. Substantial experiments on SemEval2010 Task8 dataset demonstrate that the proposed method achieves state-of-the-art performance.
期刊介绍:
The Journal of Web Semantics is an interdisciplinary journal based on research and applications of various subject areas that contribute to the development of a knowledge-intensive and intelligent service Web. These areas include: knowledge technologies, ontology, agents, databases and the semantic grid, obviously disciplines like information retrieval, language technology, human-computer interaction and knowledge discovery are of major relevance as well. All aspects of the Semantic Web development are covered. The publication of large-scale experiments and their analysis is also encouraged to clearly illustrate scenarios and methods that introduce semantics into existing Web interfaces, contents and services. The journal emphasizes the publication of papers that combine theories, methods and experiments from different subject areas in order to deliver innovative semantic methods and applications.