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Titlebook: Computational Linguistics; 14th International C Kôiti Hasida,Ayu Purwarianti Conference proceedings 2016 Springer Science+Business Media Si

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书目名称Computational Linguistics
副标题14th International C
编辑Kôiti Hasida,Ayu Purwarianti
视频video
概述Includes supplementary material:
丛书名称Communications in Computer and Information Science
图书封面Titlebook: Computational Linguistics; 14th International C Kôiti Hasida,Ayu Purwarianti Conference proceedings 2016 Springer Science+Business Media Si
描述.This bookconstitutes the refereed proceedings of the 14th International Conference of the PacificAssociation for Computational Linguistics, PACLING 2015, held in Bali,Indonesia, in May 2015...The 18revised full papers presented were carefully reviewed and selected from 45papers. The papers are organized around the following topics: syntax andsyntactic analysis; semantics and semantic analysis; spoken language anddialogue; corpora and corpus-based language processing; text and messageunderstanding; information extraction and text mining; information retrievaland question answering; language learning; machine translation..
出版日期Conference proceedings 2016
关键词Computational Linguistics; Syntax; Semantics; Spoken Language; Dialogue; Corpora; Text Mining; Information
版次1
doihttps://doi.org/10.1007/978-981-10-0515-2
isbn_softcover978-981-10-0514-5
isbn_ebook978-981-10-0515-2Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Science+Business Media Singapore 2016
The information of publication is updating

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Kontinuumsbegriff und Kinematik,d by taking into account the last morphemes of each argument as features. We apply our proposed system to the annotated corpus of semantic role labels on a balanced Japanese corpus. The experimental results show that the CRF-based labeler with features extracted by HTCT approach outperforms the normal CRF-based labeler.
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Stromfadentheorie reibungsfreier Fluide,den Markov models and conditional random fields) both in the general domain and the target domain. In addition we show the learning curve in a domain adaptation scenario. The result shows that our method outperforms state-of-the-art methods in the same domain as the training data and is better than them in domain adaptation situations as well.
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