Fatemeh Torabi Asr, J. Sonntag, Yulia Grishina, Manfred Stede
{"title":"大规模数据事件相关分析的概念和实践步骤","authors":"Fatemeh Torabi Asr, J. Sonntag, Yulia Grishina, Manfred Stede","doi":"10.3115/v1/W14-2906","DOIUrl":null,"url":null,"abstract":"A simple conceptual model is employed to investigate events, and break the task of coreference resolution into two steps: semantic class detection and similaritybased matching. With this perspective an algorithm is implemented to cluster event mentions in a large-scale corpus. Results on test data from AQUAINT TimeML, which we annotated manually with coreference links, reveal how semantic conventions vs. information available in the context of event mentions affect decisions in coreference analysis.","PeriodicalId":392223,"journal":{"name":"EVENTS@ACL","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Conceptual and Practical Steps in Event Coreference Analysis of Large-scale Data\",\"authors\":\"Fatemeh Torabi Asr, J. Sonntag, Yulia Grishina, Manfred Stede\",\"doi\":\"10.3115/v1/W14-2906\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A simple conceptual model is employed to investigate events, and break the task of coreference resolution into two steps: semantic class detection and similaritybased matching. With this perspective an algorithm is implemented to cluster event mentions in a large-scale corpus. Results on test data from AQUAINT TimeML, which we annotated manually with coreference links, reveal how semantic conventions vs. information available in the context of event mentions affect decisions in coreference analysis.\",\"PeriodicalId\":392223,\"journal\":{\"name\":\"EVENTS@ACL\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EVENTS@ACL\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3115/v1/W14-2906\",\"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/W14-2906","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Conceptual and Practical Steps in Event Coreference Analysis of Large-scale Data
A simple conceptual model is employed to investigate events, and break the task of coreference resolution into two steps: semantic class detection and similaritybased matching. With this perspective an algorithm is implemented to cluster event mentions in a large-scale corpus. Results on test data from AQUAINT TimeML, which we annotated manually with coreference links, reveal how semantic conventions vs. information available in the context of event mentions affect decisions in coreference analysis.
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