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Titlebook: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D; 16th China National Maosong Sun,Xiao

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書目名稱Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D
副標(biāo)題16th China National
編輯Maosong Sun,Xiaojie Wang,Deyi Xiong
視頻videohttp://file.papertrans.cn/226/225770/225770.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D; 16th China National  Maosong Sun,Xiao
描述.This book constitutes the proceedings of the 16th China National Conference on Computational Linguistics, CCL 2017, and the 5th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2017, held in Nanjing, China, in October 2017. ..The 39 full papers presented in this volume were carefully reviewed and selected from 272 submissions. They were organized in topical sections named: Fundamental theory and methods of computational linguistics; Machine translation and multilingual information processing; Knowledge graph and information extraction; Language resource and evaluation; Information retrieval and question answering; Text classification and summarization; Social computing and sentiment analysis; NLP applications; Minority language information processing..
出版日期Conference proceedings 2017
關(guān)鍵詞artificial intelligence; Chinese information processing; computational linguistics; information retriev
版次1
doihttps://doi.org/10.1007/978-3-319-69005-6
isbn_softcover978-3-319-69004-9
isbn_ebook978-3-319-69005-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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978-3-319-69004-9Springer International Publishing AG 2017
地板
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Maosong Sun,Xiaojie Wang,Deyi XiongIncludes supplementary material:
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/225770.jpg
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https://doi.org/10.1007/978-3-540-28396-6 In this paper, we focus on person name recognition in judgment documents. Owing to the lack of human-annotated data, we propose a joint learning approach, namely Aux-LSTM, to use a large scale of auto-annotated data to help human-annotated data (in a small size) for person name recognition. Specifi
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