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Architecture, Models, and Algorithms for Textual Similarity

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자료유형	E-Book
개인저자	He, Hua.
단체저자명	University of Maryland, College Park. Computer Science.
서명/저자사항	Architecture, Models, and Algorithms for Textual Similarity.
발행사항	[S.l.] : University of Maryland, College Park., 2018
발행사항	Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항	212 p.
소장본 주기	School code: 0117.
ISBN	9780438149212
일반주기	Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B. Adviser: Jimmy Lin.
요약	Identifying similar pieces of texts remains one of the fundamental problems in computational linguistics. This dissertation focuses on the textual similarity measurement and identification problem by studying a variety of major tasks that share
요약	We investigate how to make textual similarity measurement more accurate with deep neural networks. Traditional approaches are either based on feature engineering which leads to disconnected solutions, or the Siamese architecture which treats inp
요약	Our multi-perspective convolutional neural networks (MPCNN) uses a multiplicity of perspectives to process input sentences with multiple parallel convolutional neural networks, is able to extract salient sentence-level features automatically at
요약	We also provide an attention-based input interaction layer on top of the MPCNN model. The input interaction layer models a closer relationship of input words by converting two separate sentences into an inter-related sentence pair. This layer ut
요약	We then provide our pairwise word interaction model with very deep neural networks (PWI). This model directly encodes input word interactions with novel pairwise word interaction modeling and a novel similarity focus layer. The use of very deep
요약	We also focus on the question answering task with a pairwise ranking approach. Unlike traditional pointwise approach of the task, our pairwise ranking approach with the use of negative sampling focuses on modeling interactions between two pairs
요약	For the insight extraction on biomedical literature task, we develop neural networks with similarity modeling for better causality/correlation relation extraction, as we convert the extraction task into a similarity measurement task. Our approac
요약	Lastly, we explore how to exploit massive parallelism offered by modern GPUs for high-efficiency pattern matching. We take advantage of GPU hardware advances and develop a massive parallelism approach. We firstly work on phrase-based SMT, where
일반주제명	Computer science.
언어	영어
기본자료 저록	Dissertation Abstracts International79-11B(E). Dissertation Abstract International
대출바로가기	http://www.riss.kr/pdu/ddodLink.do?id=T14997178