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dc.contributor.advisorWu, Lei
dc.creatorHe, Han
dc.date.accessioned2019-06-13T13:57:33Z
dc.date.available2019-06-13T13:57:33Z
dc.date.created2018-05
dc.date.issued2018-05-10
dc.date.submittedMay 2018
dc.identifier.urihttps://hdl.handle.net/10657.1/1438
dc.description.abstractDependency parsing is a useful task to help computer understand human language. By parsing the dependency grammar of a sentence automatically, dependency parser produces dependency-based syntactic representations which enhance performance of many language applications, such as machine translation, question answering and information extraction. Recently dependency parsing has attracted considerable interest from researchers and developers in the Natural Language Processing field, and many state-of-art works have achieved high accuracies. But not all of them are applicable for industry applications in terms of runtime speed and memory efficiency. We implemented and evaluated various dependency parsing algorithms, finding out the most practical algorithm in consideration of tradeoff between accuracy and runtime speed. The final achievement is a practically usable dependency parser, which can parse raw sentences to grammar trees. Our parser has been released as open source software and live demonstrated on http://iparser.hankcs.com/.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectDependency Parsing
dc.subjectNatural Language Processing
dc.subjectDeep Learning
dc.titleIndustrial Strength Dependency Parsing System
dc.typeThesis
dc.date.updated2019-06-13T13:57:33Z
thesis.degree.grantorUniversity of Houston-Clear Lake
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
dc.contributor.committeeMemberYang, Xiaokun
dc.contributor.committeeMemberShih, Liwen
dc.contributor.committeeMemberLu, Jiang
dc.type.materialtext
local.embargo.terms2020-05-01
local.embargo.lift2020-05-01


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