{"title":"新闻中复杂事件提取和聚类的无监督技术","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":"{\"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}","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}
Unsupervised Techniques for Extracting and Clustering Complex Events in News
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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