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dc.rights.licenseRestricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.
dc.contributorJi, Heng
dc.contributorHendler, James A.
dc.contributorMcGuinness, Deborah L.
dc.contributor.authorPan, Xiaoman
dc.date.accessioned2021-11-03T09:14:54Z
dc.date.available2021-11-03T09:14:54Z
dc.date.created2020-08-04T12:17:55Z
dc.date.issued2019-12
dc.identifier.urihttps://hdl.handle.net/20.500.13015/2482
dc.descriptionDecember 2019
dc.descriptionSchool of Science
dc.description.abstractIn this thesis, we propose a Cross-lingual Entity Extraction and Linking framework for fine-grained types and 300 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to extract entity mentions, assign a fine-grained type to each mention, and link it to Wikipedia. We perform a series of new knowledge base mining approaches: generating “silver-standard” entity annotations, transferring annotations from English to other languages through cross-lingual links, refining annotations using self-training, deriving language-specific morphology features from anchor links, and training cross-lingual joint entity and word embedding by generating cross-lingual data which is a mix of entities and contextual words based on Wikipedia. Both entity extraction and linking results are promising on intrinsic Wikipedia data and extrinsic non-Wikipedia data.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectComputer science
dc.titleCross-lingual entity extraction and linking
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid179916
dc.digitool.pid179917
dc.digitool.pid179918
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreeMS
dc.relation.departmentDept. of Computer Science


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