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dc.contributor.advisorEtzioni, Orenen_US
dc.contributor.authorLin, Thomasen_US
dc.date.accessioned2013-04-17T18:03:03Z
dc.date.available2013-04-17T18:03:03Z
dc.date.issued2013-04-17
dc.date.submitted2012en_US
dc.identifier.otherLin_washington_0250E_11122.pdfen_US
dc.identifier.urihttp://hdl.handle.net/1773/22599
dc.descriptionThesis (Ph.D.)--University of Washington, 2012en_US
dc.description.abstractThe Web contains more text than any other source in human history, and continues to expand rapidly. Computer algorithms to process and extract knowledge from Web text have the potential not only to improve Web search, but also to collect a sizable fraction of human knowledge and use it to enable smarter artificial intelligence. To scale to the size and diversity of the Web, many Web text processing algorithms use domain-independent statistical approaches, rather than limiting their processing to any fixed ontologies or sets of domains. While traditional knowledge bases (KBs) had limited coverage of general knowledge, the last few years have seen the rapid rise of new KBs like Freebase and Wikipedia that now cover millions of general interest topics. While these KBs still do not cover the full diversity of the Web, this thesis demonstrates that they are now close enough that there are ways to effectively leverage them in domain-independent Web text processing. It presents and empirically verifies how these KBs can be used to filter uninteresting Web extractions, enhance understanding and usability of both extracted relations and extracted entities, and even power new functionality for Web search. The effective integration of KBs with automated Web text processing brings us closer toward realizing the potential of Web text.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.rightsCopyright is held by the individual authors.en_US
dc.subjectArtificial Intelligence; Entity Linking; Information Extraction; Knowledge Acquisition; Natural Language Processing; Wikipediaen_US
dc.subject.otherComputer scienceen_US
dc.subject.othercomputer science and engineeringen_US
dc.titleLeveraging Knowledge Bases in Web Text Processingen_US
dc.typeThesisen_US
dc.embargo.termsNo embargoen_US


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