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Titlebook: Computational Linguistics and Intelligent Text Processing; 16th International C Alexander Gelbukh Conference proceedings 2015 Springer Inte

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書目名稱Computational Linguistics and Intelligent Text Processing
副標(biāo)題16th International C
編輯Alexander Gelbukh
視頻videohttp://file.papertrans.cn/233/232597/232597.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computational Linguistics and Intelligent Text Processing; 16th International C Alexander Gelbukh Conference proceedings 2015 Springer Inte
描述.The two volumes LNCS 9041 and 9042 constitute the proceedings of the 16th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2015, held in Cairo, Egypt, in April 2015. .The total of 95 full papers presented was carefully reviewed and selected from 329 submissions. They were organized in topical sections on grammar formalisms and lexical resources; morphology and chunking; syntax and parsing; anaphora resolution and word sense disambiguation; semantics and dialogue; machine translation and multilingualism; sentiment analysis and emotion detection; opinion mining and social network analysis; natural language generation and text summarization; information retrieval, question answering, and information extraction; text classification; speech processing; and applications..
出版日期Conference proceedings 2015
關(guān)鍵詞Artificial intelligence; Computational linguistics; Document categorization; HCI; Human language technol
版次1
doihttps://doi.org/10.1007/978-3-319-18117-2
isbn_softcover978-3-319-18116-5
isbn_ebook978-3-319-18117-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

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Text Integrity Assessment: Sentiment Profile vs Rhetoric Structure) shows the stronger contribution of rhetoric structure information in comparison with the sentiment profile information. Both above learning approaches demonstrated that discourse structure as obtained by RST parser is sufficient to conduct the text integrity assessment. At the same time, sentiment
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,Grundgleichungen der Str?mungsmechanik,tects trending sentimental topic in the shortest time. After a combination of multiple features, e.g. tweet volume and user volume, it demonstrates impressive effectiveness with 82.3% recall and surpasses all the competitors.
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