By Alexander Gelbukh
This two-volume set, inclusive of LNCS 8403 and LNCS 8404, constitutes the completely refereed complaints 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 offered including four invited papers have been conscientiously reviewed and chosen from three hundred submissions. The papers are geared up within the following topical sections: lexical assets; rfile illustration; morphology, POS-tagging, and named entity reputation; syntax and parsing; anaphora answer; spotting textual entailment; semantics and discourse; ordinary language new release; sentiment research and emotion reputation; opinion mining and social networks; computing device translation and multilingualism; info retrieval; textual content class and clustering; textual content summarization; plagiarism detection; type and spelling checking; speech processing; and applications.
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Additional resources for Computational Linguistics and Intelligent Text Processing: 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part I
Portuguese and apply the state-of-the-art verb clustering approach developed for English  to this language. Using the NLP tools developed for Br. Portuguese for feature extraction, we experiment with the same basic features and the same clustering method as for English. The results are encouraging and support the hypothesis that Levin-style classes can indeed be cross-linguistically applicable: it was possible to obtain a gold standard largely via translation from English to Br. Portuguese, and the best performing features and clustering techniques matched with those for English and French.
We are currently using this last method since data annotation is very costly. However, the first approach would certainly lead to more accurate results. The weight and the ranking of the different constraints must then be examined. A linear model can provide a first approximation but there are surely better ways to integrate the different constraints. Some studies provide some cues but they need to be proper evaluated in order to be integrated in this framework . 3 Manual Validation Lastly, the approach requires a manual validation.
The filter incorporates special heuristics for cases where this assumption tends to generate too many errors. With prepositional SCFs involving one PP or more, the filter determines which one is the less frequent PP. It then re-assigns the associated frequency to the same SCF without this PP. For example, SP[sur+SN] SP[en+SN] could be split to 2 SCFs : SP[sur+SN] and SP[en+SN]. In this example, SP[en+SN] is the less frequent prepositional phrase and the final SCF for the sentence (1) is (5). (5) SP[sur+SN] Note that SP[en+SN] is here an adjunct.
Computational Linguistics and Intelligent Text Processing: 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part I by Alexander Gelbukh
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