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dc.contributor.authorSequiera, Royal
dc.date.accessioned2018-08-10 14:18:56 (GMT)
dc.date.available2018-08-10 14:18:56 (GMT)
dc.date.issued2018-08-10
dc.date.submitted2018
dc.identifier.urihttp://hdl.handle.net/10012/13570
dc.description.abstractWith the advent of deep learning methods, researchers have been increasingly preferring deep learning methods over decades-old feature-engineering-inspired work in Natural Language Processing (NLP). The research community has been moving away from otherwise dominant feature engineering approaches; rather, is gravitating towards more complicated neural architectures. Highly competitive tools like part-of-speech taggers that exhibit human-like accuracy are traded off for complex networks, with the hope that the neural network will learn the features needed. In fact, there have been efforts to do NLP "from scratch" with neural networks that altogether eschew featuring engineering based tools (Collobert et al, 2011). In our research, we modify the input that is fed to neural networks by annotating the input with linguistic information: POS tags, Named Entity Recognition output, linguistic relations, etc. With just the addition of these linguistic features on a simple Siamese convolutional neural network, we are able to achieve state-of-the-art results. We argue that this strikes a better balance between feature vs. network engineering.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectNatural Language Processingen
dc.subjectNLPen
dc.subjectQuestion Answeringen
dc.subjectAnswer selectionen
dc.subjectCNNen
dc.subjectFeature engineeringen
dc.subjectneural networksen
dc.titleSimple Convolutional Neural Networks with Linguistically-Annotated Input for Answer Selection in Question Answeringen
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.advisorLin, Jimmy
uws.contributor.affiliation1Faculty of Mathematicsen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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