Computational Linguistics and Intelligent Text Processing: by Alexander Gelbukh

By Alexander Gelbukh

This two-volume set, including LNCS 8403 and LNCS 8404, constitutes the completely refereed complaints of the 14th foreign convention on clever textual content Processing and Computational Linguistics, CICLing 2014, held in Kathmandu, Nepal, in April 2014. The eighty five revised papers awarded including four invited papers have been rigorously reviewed and chosen from three hundred submissions. The papers are geared up within the following topical sections: lexical assets; record illustration; morphology, POS-tagging, and named entity reputation; syntax and parsing; anaphora solution; spotting textual entailment; semantics and discourse; usual language iteration; sentiment research and emotion popularity; opinion mining and social networks; laptop translation and multilingualism; details retrieval; textual content type and clustering; textual content summarization; plagiarism detection; variety and spelling checking; speech processing; and applications.

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Computational Linguistics and Intelligent Text Processing: 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part I

This two-volume set, together with LNCS 8403 and LNCS 8404, constitutes the completely refereed court cases of the 14th overseas convention on clever textual content Processing and Computational Linguistics, CICLing 2014, held in Kathmandu, Nepal, in April 2014. The eighty five revised papers awarded including four invited papers have been rigorously reviewed and chosen from three hundred submissions.

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Extra info for Computational Linguistics and Intelligent Text Processing: 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part I

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A System for Large-Scale Acquisition of Verbal, Nominal and Adjectival Subcategorization Frames from Corpora. In: Proceedings of the Meeting of the Association for Computational Linguistics, Prague, pp. 912–918 (2007) 21. : Syntex, analyseur syntaxique de corpus. In: Actes des 12`emes Journ´ees sur le Traitement Automatique des Langues Naturelles, Dourdan (2005) 24 T. Poibeau 22. : Statistical Filtering and Subcategorization Frame Acquisition. In: Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, Hong Kong (2000) 23.

If a preposition is the head of one of the dependencies, the module explores the syntactic analysis to find if it is followed by a noun phrase (+SN]) or an infinitive verb (+SINF]). (3) shows the output of the pattern extractor for the input in (1). 3 GEN (The SCF Builder) The SCF builder extracts SCF candidates for each verb from the output of the pattern extractor and calculates the number of corpus occurrences for each SCF and verb combination. The syntactic constituents used for building the SCFs are the following: 1.

Finally, we show how a better representation of the constraints used would yield better results. 1 Introduction Natural Language Processing (NLP) aims at developing techniques for processing natural language texts using computers. ). Unfortunately, such resources are not available for most languages and are very costly to develop manually. A recent trend of research has tried to overcome these limitations through the development of automatic acquisition methods from corpora. Automatic lexical acquisition is an engineering task aiming at providing comprehensive—even if not fully accurate—resources for NLP.

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