By Mike Thelwall, Kevan Buckley, George Paltoglou, Marcin Skowron, David Garcia (auth.), Alexander Gelbukh (eds.)
ISBN-10: 3642372554
ISBN-13: 9783642372551
ISBN-10: 3642372562
ISBN-13: 9783642372568
This two-volume set, together with LNCS 7816 and LNCS 7817, constitutes the completely refereed court cases of the thirteenth overseas convention on desktop Linguistics and clever Processing, CICLING 2013, hung on Samos, Greece, in March 2013. the full of ninety one contributions provided was once rigorously reviewed and chosen for inclusion within the complaints. The papers are prepared in topical sections named: normal ideas; lexical assets; morphology and tokenization; syntax and named entity reputation; be aware experience disambiguation and coreference answer; semantics and discourse; sentiment, polarity, subjectivity, and opinion; computer translation and multilingualism; textual content mining, details extraction, and data retrieval; textual content summarization; stylometry and textual content simplification; and applications.
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Additional resources for Computational Linguistics and Intelligent Text Processing: 14th International Conference, CICLing 2013, Samos, Greece, March 24-30, 2013, Proceedings, Part II
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Agarwal and N. Mittal Minimum Redundancy Maximum Relevance (mRMR) The Minimum Redundancy Maximum Relevance (mRMR) feature selection method [12] is used to identify the discriminant features of a class. mRMR method selects features those have high dependency to class (maximum relevancy) and minimum dependency among features (minimum redundancy). Sometimes relevant features with maximum relevancy with the class may have redundancy among features. When two features have redundancy then if one feature is eliminated, there is not much difference in class discrimination [12].
Firstly feature set using unigram (F1 feature set) and bi-gram (F2 feature set) features are generated. Bi-gram based features (F2) are capable of handling negation words in the context of the text [2] that is why there is no need of negation handling explicitly in this case. 18 B. Agarwal and N. Mittal Further, prominent feature sets and composite feature sets are created from unigram and bi-gram features. Prominent features are extracted from unigrams with IG and mRMR, we call it as PIGF1 (Prominent IG Features 1-gram) and PmRMRF1 (Prominent mRMR Features 1-gram) respectively.
In: Empirical Methods in Natural Language Processing (EMNLP 2009), pp. 170–179 (2009) 26. : Lexicon-based methods for sentiment analysis. Computational Linguistics 37(2), 267–307 (2011) 27. : Topic-based sentiment analysis for the social web: The role of mood and issue-related words. Journal of the American Society for Information Science and Technology (in press) 28. : Emotion homophily in social network site messages. php/fm/ar ticle/view/2897/2483 (retrieved March 6, 2011) 29. : Sentiment in twitter events.
Computational Linguistics and Intelligent Text Processing: 14th International Conference, CICLing 2013, Samos, Greece, March 24-30, 2013, Proceedings, Part II by Mike Thelwall, Kevan Buckley, George Paltoglou, Marcin Skowron, David Garcia (auth.), Alexander Gelbukh (eds.)
by George
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