COMBINING TEXT EMBEDDING WITH ADDITIONAL KNOWLEDGE FOR INFORMATION EXTRACTION

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dc.contributor.advisorPan, Shimei
dc.contributor.authorRoy, Arpita
dc.contributor.departmentInformation Systems
dc.contributor.programInformation Systems
dc.date.accessioned2022-02-09T15:52:46Z
dc.date.available2022-02-09T15:52:46Z
dc.date.issued2020-01-01
dc.description.abstractInformation Extraction (IE) is an essential field of natural language processing (NLP). Over the years, researchers have studied numerous approaches and techniques to meet the challenges of different IE tasks. This dissertations explores various knowledge fusion techniques to combine diverse domain-independent and domain-specific knowledge with multiple text embedding techniques for effective IE. Specifically, this work presents a systematic investigation to combine different types of knowledge (e.g., lexical, syntactic, semantic, and domain knowledge) with different text embedding techniques (e.g., static and contextual embeddings) to achieve the state of the art performance in several IE tasks (e.g., Open IE, malware attribute identification and clinical relation extraction).
dc.formatapplication:pdf
dc.genredissertations
dc.identifierdoi:10.13016/m2tlmg-nlvd
dc.identifier.other12429
dc.identifier.urihttp://hdl.handle.net/11603/24196
dc.languageen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Theses and Dissertations Collection
dc.relation.ispartofUMBC Graduate School Collection
dc.relation.ispartofUMBC Student Collection
dc.sourceOriginal File Name: Roy_umbc_0434D_12429.pdf
dc.subjectInformation Extraction
dc.subjectNatural Language Processing
dc.subjectWord Embedding
dc.titleCOMBINING TEXT EMBEDDING WITH ADDITIONAL KNOWLEDGE FOR INFORMATION EXTRACTION
dc.typeText
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