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Titlebook: Computational Linguistics and IntelligentText Processing; 20th International C Alexander Gelbukh Conference proceedings 2023 Springer Natur

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發(fā)表于 2025-3-21 19:46:52 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Computational Linguistics and IntelligentText Processing
副標題20th International C
編輯Alexander Gelbukh
視頻videohttp://file.papertrans.cn/233/232590/232590.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computational Linguistics and IntelligentText Processing; 20th International C Alexander Gelbukh Conference proceedings 2023 Springer Natur
描述The two-volume set LNCS 13451 and 13452 constitutes revised selected papers from the CICLing 2019 conference which took place in La Rochelle, France, April 2019..The total of 95 papers presented in the two volumes was carefully reviewed and selected from 335 submissions. The book also contains 3 invited papers. ..The papers are organized in the following topical sections: General, Information extraction, Information retrieval, Language modeling, Lexical resources, Machine translation, Morphology, sintax, parsing, Name entity recognition, Semantics and text similarity, Sentiment analysis, Speech processing, Text categorization, Text generation, and Text mining..
出版日期Conference proceedings 2023
關(guān)鍵詞artificial intelligence; automata theory; computational linguistics; computer science; computer systems;
版次1
doihttps://doi.org/10.1007/978-3-031-24337-0
isbn_softcover978-3-031-24336-3
isbn_ebook978-3-031-24337-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2023
The information of publication is updating

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Detection and?Analysis of?Drug Non-compliance in?Internet Fora Using Information Retrieval Approachen drug non-compliance information. Exploitation of information retrieval methods permits to detect non-compliance messages with up to 0.529 F-measure, compared to 0.824 F-measure reached with supervized machine learning methods. For some fine-grained categories and on new data, it shows up to 0.70 Precision.
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0302-9743 modeling, Lexical resources, Machine translation, Morphology, sintax, parsing, Name entity recognition, Semantics and text similarity, Sentiment analysis, Speech processing, Text categorization, Text generation, and Text mining..978-3-031-24336-3978-3-031-24337-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
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,?hnlichkeitsgesetze der Str?mungslehre, neither of the two arguments – and as such present a challenge for discourse parsers. We find syntactic patterns for this phenomenon and describe it linguistically on the basis of Czech discourse-annotated corpus material, with the aim to facilitate an automatic detection of such connectives and a correct localization of their arguments.
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