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Titlebook: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D; 17th China National Maosong Sun,Ting

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樓主
發(fā)表于 2025-3-21 18:14:25 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D
副標題17th China National
編輯Maosong Sun,Ting Liu,Yang Liu
視頻videohttp://file.papertrans.cn/226/225767/225767.mp4
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; 17th China National  Maosong Sun,Ting
描述.This book constitutes the proceedings of the 17th China National Conference on Computational Linguistics, CCL 2018, and the 6th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2018, held in Changsha, China, in October 2018...The 33 full papers presented in this volume were carefully reviewed and selected from 84 submissions. They are organized in topical sections named: Semantics; machine translation; knowledge graph and information extraction; linguistic resource annotation and evaluation; information retrieval and question answering; text classification and summarization; social computing and sentiment analysis; and NLP applications..
出版日期Conference proceedings 2018
關鍵詞artificial intelligence; classification; information extraction; language resources; machine translation
版次1
doihttps://doi.org/10.1007/978-3-030-01716-3
isbn_softcover978-3-030-01715-6
isbn_ebook978-3-030-01716-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2018
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 20:53:24 | 只看該作者
https://doi.org/10.1007/978-3-030-01716-3artificial intelligence; classification; information extraction; language resources; machine translation
板凳
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/225767.jpg
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Reaction Patterns of the Lymph Nodeer as the minimum processing unit of the text, without using the semantic information about Chinese characters and the radicals in Chinese words. To this end, we proposed a radical enhanced Chinese word embedding in this paper. The model uses conversion and radical escaping mechanisms to extract the
6#
發(fā)表于 2025-3-22 13:20:22 | 只看該作者
H.-H. Wacker,H. J. Radzun,M. R. Parwaresche content of a sentence and the syntactic structures constitute the framework of a sentence. How to combine both aspects and exploit their common advantages is a challenging issue. In this paper, we propose a Principal Stylistic Features Analysis method (PSFA) to combine these two parts, and then mi
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發(fā)表于 2025-3-23 00:31:28 | 只看該作者
Antonio Laganà,Antonio Riganellineral the length proportionality assumption that the lengths of sentences in one language tend to be proportional to that of their translations, and are known to bear poor adaptivity to new languages and corpora. In this paper, we attempt to interpret this assumption from a new perspective via the n
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