{"title":"Unsupervised Techniques for Extracting and Clustering Complex Events in News","authors":"Delia Rusu, James Hodson, Anthony Kimball","doi":"10.3115/v1/W14-2905","DOIUrl":null,"url":null,"abstract":"Structured machine-readable representations of news articles can radically change the way we interact with information. One step towards obtaining these representations is event extraction - the identification of event triggers and arguments in text. With previous approaches mainly focusing on classifying events into a small set of predefined types, we analyze unsupervised techniques for complex event extraction. In addition to extracting event mentions in news articles, we aim at obtaining a more general representation by disambiguating to concepts defined in knowledge bases. These concepts are further used as features in a clustering application. Two evaluation settings highlight the advantages and shortcomings of the proposed approach.","PeriodicalId":392223,"journal":{"name":"EVENTS@ACL","volume":"128 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EVENTS@ACL","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3115/v1/W14-2905","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 19
Abstract
Structured machine-readable representations of news articles can radically change the way we interact with information. One step towards obtaining these representations is event extraction - the identification of event triggers and arguments in text. With previous approaches mainly focusing on classifying events into a small set of predefined types, we analyze unsupervised techniques for complex event extraction. In addition to extracting event mentions in news articles, we aim at obtaining a more general representation by disambiguating to concepts defined in knowledge bases. These concepts are further used as features in a clustering application. Two evaluation settings highlight the advantages and shortcomings of the proposed approach.
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