Download Computational Linguistics and Intelligent Text Processing: by Nicoletta Calzolari, Claudia Soria (auth.), Alexander PDF

By Nicoletta Calzolari, Claudia Soria (auth.), Alexander Gelbukh (eds.)

ISBN-10: 3642121152

ISBN-13: 9783642121159

th CICLing 2010 was once the eleven Annual convention on clever textual content Processing and Computational Linguistics. The CICLing meetings supply a wide-scope discussion board for dialogue of the artwork and craft of usual language processing study in addition to the simplest practices in its purposes. This quantity includes 3 invited papers and the average papers permitted for oral presentation on the convention. The papers authorized for poster pres- tation have been released in a distinct factor of one other magazine (see details on thewebsite).Since 2001,theproceedingsofCICLingconferenceshavebeen p- lished in Springer’s Lecture Notes in laptop technology sequence, as volumes 2004, 2276, 2588, 2945, 3406, 3878, 4394, 4919, and 5449. the amount is based into 12 sections: – Lexical assets – Syntax and Parsing – note experience Disambiguation and Named Entity acceptance – Semantics and conversation – Humor and feelings – computing device Translation and Multilingualism – details Extraction – info Retrieval – textual content Categorization and Classi?cation – Plagiarism Detection – textual content Summarization – Speech new release The 2010 occasion obtained a list excessive variety of submissions within the - yr heritage of the CICLing sequence. a complete of 271 papers by way of 565 authors from forty seven countriesweresubmittedforevaluationbytheInternationalProgramCommittee (see Tables 1 and 2). This quantity includes revised models of sixty one papers, by way of 152 authors, chosen for oral presentation; the attractiveness fee used to be 23%.

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Additional resources for Computational Linguistics and Intelligent Text Processing: 11th International Conference, CICLing 2010, Iaşi, Romania, March 21-27, 2010. Proceedings

Sample text

The HMM system retrieves all tokens without relying on post processing. The Viterbi algorithm in fact is applied for each role to the entire sentence. 02 un ' opportunità avvicinare per l ' Unione Europea ai cittadini is: italian sentence an opportunity to the bring European Union closer to the people (B) S1 un ' opportunità per avvicinare l ' Unione Europea ai cittadini S2 un ' opportunità per avvicinare l ' Unione Europea ai cittadini S3 un ' opportunità per avvicinare l ' Unione Europea ai cittadini Fig.

01 in ILF-WN, will be used to describe the resource in more detail. Being a formalization of WordNet’s glosses, ILF-WN is structured in synsets, namely, in senses expressed by a set of synonym words and a gloss. As shown in Example 3 every element in ILF-WN has three attributes: A unique numeric identifier or offset, its POS category in WordNet notation (‘a’ for adjectives, ‘s’ for satellite adjectives, ‘r’ for adverbs, ‘v’ for verbs and ‘n’ for nouns), and the synset name, which consists of a lemma, its POS category and the sense number.

Even though its reported accuracy is similar to those of Stanford [23] and C&C tools [19] POS taggers also trained on the Penn Treebank, we chose CRFTagger due to its speed in processing large collections of documents. Dependency parser. We feed the POS tagged glosses to the Stanford Parser [24] in order to output a syntactic analysis consisting of Stanford typed dependencies, which amount to a kind of grammatical relations between lemmatized words acting as nodes of a dependency graph [18]. We take advantage of the parser’s ability to output the dependency graphs in XML format for a better integration in ILF-WN.

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Computational Linguistics and Intelligent Text Processing: 11th International Conference, CICLing 2010, Iaşi, Romania, March 21-27, 2010. Proceedings by Nicoletta Calzolari, Claudia Soria (auth.), Alexander Gelbukh (eds.)


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