A General FOFE-net Framework for Simple and Effective Question Answering over Knowledge Bases

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dc.contributor.advisorJiang, Hui
dc.contributor.authorWu, Dekun
dc.date.accessioned2019-11-22T18:38:05Z
dc.date.available2019-11-22T18:38:05Z
dc.date.copyright2019-04
dc.date.issued2019-11-22
dc.date.updated2019-11-22T18:38:05Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractQuestion answering over knowledge base (KB-QA) has recently become a popular research topic in NLP. One of the popular ways to solve the KBQA problem is to make use of a pipeline of several NLP modules, including entity discovery and linking (EDL) and relation detection. Recent success on KBQA task usually involves complex network structures with sophisticated heuristics. Inspired by a previous work that builds a strong KBQA baseline, we propose a simple but general neural model composed of fixed-size ordinally forgetting encoding (FOFE) and deep neural networks, called FOFE-net to solve KB-QA problem at different stages. For evaluation, we use two popular KB-QA datasets, SimpleQuestions, WebQSP, and our newly created dataset, FreebaseQA. The experimental results show that FOFE-net performs well on KBQA subtasks, entity discovery and linking (EDL) and relation detection, and in turn pushing overall KB-QA system to achieve strong results on all the datasets.
dc.identifier.urihttp://hdl.handle.net/10315/36672
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer engineering
dc.subject.keywordsKnowledge Base Question Answering
dc.subject.keywordsMachine Learning
dc.subject.keywordsNatural Language Processing
dc.titleA General FOFE-net Framework for Simple and Effective Question Answering over Knowledge Bases
dc.typeElectronic Thesis or Dissertation

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