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Titlebook: Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing; Stefan Wermter,Ellen Riloff,Gabriele Schel

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书目名称Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing
编辑Stefan Wermter,Ellen Riloff,Gabriele Scheler
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing;  Stefan Wermter,Ellen Riloff,Gabriele Schel
描述This book is based on the workshop on New Approaches to Learning for Natural Language Processing, held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI‘95, in Montreal, Canada in August 1995..Most of the 32 papers included in the book are revised selected workshop presentations; some papers were individually solicited from members of the workshop program committee to give the book an overall completeness. Also included, and written with the novice reader in mind, is a comprehensive introductory survey by the volume editors. The volume presents the state of the art in the most promising current approaches to learning for NLP and is thus compulsory reading for researchers in the field or for anyone applying the new techniques to challenging real-world NLP problems.
出版日期Conference proceedings 1996
关键词Algorithmisches Lernen; Computational Learning; Grammatical Inferenz; Grammatische Inferenz; Learning Al
版次1
doihttps://doi.org/10.1007/3-540-60925-3
isbn_softcover978-3-540-60925-4
isbn_ebook978-3-540-49738-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 1996
The information of publication is updating

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X. B. Reed Jr.,L. Spiegel,S. Hartlandkind of discriminatory power provided by the Principles and Parameters linguistic framework, or Government and Binding theory. We investigate the following models: feed-forward neural networks, Frasconi-Gori-Soda and Back-Tsoi locally recurrent neural networks, Williams and Zipser and Elman recurren
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Alexander J. Smits,Jean-Paul Dussaugee refinement and network learning. This paper describes a decompositional rule extraction technique which generates rules governing the firing of individual nodes in a feedforward neural network. The technique employs heuristics to reduce the complexity in searching for rules which explain the behav
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https://doi.org/10.1007/b137383res for the description of relevant meanings of plural definiteness. A small training set (30 sentences) was created by linguistic criteria, and a functional mapping from the semantic feature representation to the overt category of indefinite/definite article was learned. The learned function was ap
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Boundary Layer Turbulence Behavior, besides the necessary input is the analysis of the various text and document structures. In our prototype CONCAT we use neural network technology to learn about the relations within the concept and document space of an existing domain. The results are quite encouraging because with existing input d
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https://doi.org/10.1007/b137383stem using a large number of connectionist and symbolic modules. Our system SCREEN learns a flat syntactic and semantic analysis of incremental streams of word hypothesis sequences. In this paper we focus on techniques for improving the quality of pruned hypotheses from a speech recognizer using aco
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Alexander J. Smits,Jean-Paul Dussauge linguistic characteristics. This paper presents SKOPE, a connectionist/symbolic spoken Korean processing engine, emphasizing that: 1) connectionist and symbolic techniques must be selectively applied according to their relative strength and weakness, and 2) linguistic characteristics of Korean must
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