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Multi-Task Graph-Based Information Extraction with Global Context

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자료유형학위논문
서명/저자사항Multi-Task Graph-Based Information Extraction with Global Context.
개인저자Luan, Yi.
단체저자명University of Washington. Electrical and Computer Engineering.
발행사항[S.l.]: University of Washington., 2019.
발행사항Ann Arbor: ProQuest Dissertations & Theses, 2019.
형태사항115 p.
기본자료 저록Dissertations Abstracts International 81-04A.
Dissertation Abstract International
ISBN9781088305737
학위논문주기Thesis (Ph.D.)--University of Washington, 2019.
일반주기 Source: Dissertations Abstracts International, Volume: 81-04, Section: A.
Advisor: Ostendorf, Mari
이용제한사항This item must not be sold to any third party vendors.This item must not be added to any third party search indexes.
요약With growing numbers of written documents in the world, it is crucial to leverage automatic language processing so that people can make better use of the information. The main challenge stems from the fact that the information in written text is not as easily used as information in a structured database. Therefore it is very important to understand and automatically extract structured information from large amount of unstructured texts. To tackle this problem, Information Extraction (IE) is the widely studied task of retrieving structured information from text. In this thesis, our goal is to develop a general high performance IE system that can work across many different domains and tasks, but particularly the less well studied domain of scientific literature.Towards achieving this goal, we propose a series of general IE frameworks that addresses the task of entity recognition, relation extraction and coreference resolution. This thesis research addresses challenges common to all such IE systems: 1) how to leverage large unannotated data when annotated training data are limited
일반주제명Computer science.
Artificial intelligence.
Information science.
언어영어
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