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dc.contributor.authorPeng, Zhongyu
dc.date.accessioned2016-08-24 17:07:03 (GMT)
dc.date.available2016-08-24 17:07:03 (GMT)
dc.date.issued2016-08-24
dc.date.submitted2016
dc.identifier.urihttp://hdl.handle.net/10012/10680
dc.description.abstractWe present a novel bag-of-words based approach that automatically constructs a semantic parsing based question answering (QA) system tailored to single-entity-single-relation questions. Given a large community QA pair corpus and a knowledge base, our approach uses knowledge base entries to supervise relation extraction from the corpus, reduces noise in the extracted data via unsupervised clustering, and learns to identify each relation’s question patterns. We implement the approach on a large Chinese corpus with little annotation, which we believe is one of the first of its kind. Experiments show that our implementation manages to answer questions in test cases independent of the corpus with relatively high accuracy and to avoid answering questions beyond its scope, achieving a high accuracy on answered questions.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.titleSingle-Entity-Single-Relation Question Answering with Minimal Annotationen
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentDavid R. Cheriton School of Computer Scienceen
uws-etd.degree.disciplineComputer Scienceen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Mathematicsen
uws.contributor.advisorLi, Ming
uws.contributor.affiliation1Faculty of Mathematicsen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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