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

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書目名稱Computational Linguistics and IntelligentText Processing
副標(biāo)題20th International C
編輯Alexander Gelbukh
視頻videohttp://file.papertrans.cn/233/232588/232588.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Computer Science; Informatics; Conference Proceedings; Research; Applications
版次1
doihttps://doi.org/10.1007/978-3-031-24340-0
isbn_softcover978-3-031-24339-4
isbn_ebook978-3-031-24340-0Series 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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A Hybrid Generative/Discriminative Model for?Rapid Prototyping of?Domain-Specific Named Entity Recoga, and thus permits the iterative and incremental design of named entity (NE) classes for new domains. The proposed model is a hybrid of a generative model named PYHSMM and a semi-Markov CRF-based discriminative model, which play complementary roles in generalizing seed terms and in distinguishing b
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A Computational Approach to?Measuring the?Semantic Divergence of?Cognatess. Semantic divergence in related languages is a key concern of historical linguistics. In this paper we investigate semantic divergence across languages by measuring the semantic similarity of cognate sets in multiple languages. The method that we propose is based on cross-lingual word embeddings.
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