Event Nugget Detection using Thresholding and Classification Techniques

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dc.contributor.authorSatyapanich, Taneeya
dc.contributor.authorFinin, Tim
dc.date.accessioned2018-10-17T16:54:18Z
dc.date.available2018-10-17T16:54:18Z
dc.date.issued2016-11-14
dc.descriptionProceedings of the 9th Text Analysis Workshopen_US
dc.description.abstractThis paper describes the Event Nugget Detection system that we submitted to the TAC KBP 2016 Event Track. We sent out two runs; UMBC1 and UMBC2. UMBC1 is a sentence-level classification system based on Convolution Neural Network and applied the probability to select a word as an event nugget. UMBC2 is the classification model trained from our features using Weka and filtered out low confidence prediction output using threshold. Our performance was low; we got F1 measure of 34.14 for UMBC1 and 35.24 for UMBC2.en_US
dc.description.urihttps://tac.nist.gov/publications/2015/participant.papers/TAC2015.UMBC.proceedings.pdfen_US
dc.format.extent5 pagesen_US
dc.genreconference paperen_US
dc.identifierdoi:10.13016/M29C6S476
dc.identifier.urihttp://hdl.handle.net/11603/11584
dc.language.isoen_USen_US
dc.publisherNational Institute of Standards and Technologyen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectEvent Nugget Detectionen_US
dc.subjectUMBC2en_US
dc.subjectprobabilityen_US
dc.subjectConvolution Neural Networken_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.titleEvent Nugget Detection using Thresholding and Classification Techniquesen_US
dc.typeTexten_US

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