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Titlebook: Computational Linguistics and Intelligent Text Processing; 15th International C Alexander Gelbukh Conference proceedings 2014 Springer-Verl

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樓主
發(fā)表于 2025-3-21 17:22:39 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Computational Linguistics and Intelligent Text Processing
副標題15th International C
編輯Alexander Gelbukh
視頻videohttp://file.papertrans.cn/233/232606/232606.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computational Linguistics and Intelligent Text Processing; 15th International C Alexander Gelbukh Conference proceedings 2014 Springer-Verl
描述This two-volume set, consisting of LNCS 8403 and LNCS 8404, constitutes the thoroughly refereed proceedings of the 14th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2014, held in Kathmandu, Nepal, in April 2014. The 85 revised papers presented together with 4 invited papers were carefully reviewed and selected from 300 submissions. The papers are organized in the following topical sections: lexical resources; document representation; morphology, POS-tagging, and named entity recognition; syntax and parsing; anaphora resolution; recognizing textual entailment; semantics and discourse; natural language generation; sentiment analysis and emotion recognition; opinion mining and social networks; machine translation and multilingualism; information retrieval; text classification and clustering; text summarization; plagiarism detection; style and spelling checking; speech processing; and applications.
出版日期Conference proceedings 2014
關鍵詞clustering and classification; document management and text processing; information retrieval; informat
版次1
doihttps://doi.org/10.1007/978-3-642-54906-9
isbn_softcover978-3-642-54905-2
isbn_ebook978-3-642-54906-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2014
The information of publication is updating

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https://doi.org/10.1007/978-3-642-54906-9clustering and classification; document management and text processing; information retrieval; informat
板凳
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地板
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https://doi.org/10.1007/978-3-030-37285-9 of time, record all written or spoken input, and compare this data to the corpus in question. As this is not very practical, we suggest here a more indirect way to do this. Previous work suggests that people’s word associations can be derived from corpus statistics. These word associations are know
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Kayleigh O’Donnell,Amy L. Reschlyproduces and implements the main components of Optimality Theory. However, we formulate the hypothesis that some of its limitations are mainly due to a poor representation of the constraints used. Finally, we show how a better representation of the constraints used would yield better results.
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Lisa M. Hagermoser Sanetti,Hao-Jan Luhlications. However, lexical resources which provide information about such classes are only available for a handful of worlds languages. Because manual development of such resources is extremely time consuming and cannot reliably capture domain variation in classification, methods for automatic indu
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https://doi.org/10.1007/978-3-030-37285-9ed scope without considering the benefits of including domain knowledge to a general ontology. Furthermore, most existing resources lack meta-information about association strength (weights) and annotations (frequency information like ., . ... or relevance information like . or .). In this paper, we
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https://doi.org/10.1007/978-981-97-0558-0lysis tools and resources. Social media posts, especially those made by the younger generation, are usually written using colloquial Arabic and include a lot of slang, many of which evolves over time. While some work has been carried out to build modern standard Arabic sentiment lexicons, these need
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