By Alexander Gelbukh

This two-volume set, which include 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 offered including four invited papers have been conscientiously reviewed and chosen from three hundred submissions. The papers are prepared within the following topical sections: lexical assets; record illustration; morphology, POS-tagging, and named entity popularity; syntax and parsing; anaphora answer; spotting textual entailment; semantics and discourse; ordinary language iteration; sentiment research and emotion popularity; opinion mining and social networks; laptop translation and multilingualism; info retrieval; textual content type and clustering; textual content summarization; plagiarism detection; type and spelling checking; speech processing; and applications.

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In: NAACL, pp. 173–180. Association for Computational Linguistics (2003) 22. : semantic orientation applied to unsupervised classification of reviews. In: ACL, pp. 417–424. Association for Computational Linguistics (2002) 23. : Learning subjective adjectives from corpora. In: Proceedings of the National Conference on Artificial Intelligence, pp. 735–741 (2000) 24. : Development and use of a gold-standard data set for subjectivity classifications. In: ACL, pp. 246–253 (1999) 25. : Learning to disambiguate potentially subjective expressions.

Finally, the low CCS of all models in the AVEC2012 may indicate that CCS is not the best evaluation metric for this task. CCS evaluates average performance of the classifier for predicting values of all data in the corpus. However, occurrences of strong emotions are relatively rare in conversations, which makes the values of a large portion of the data unsuitable for classifiers that are designed to predict such emotions. Therefore, a more appropriate evaluation metric is needed. One possible alternative would be to detect emotionally strong events first, using methods such as those previously used for the detection of hot spots in meetings [29], and only evaluate model performance on these segments.

549). These results verified our hypothesis that our high-level ASM features are more predictive then the low-level LBP features. Word-Level Emotion Recognition Using High-Level Features 27 Table 3. 5 Fig. 4. 3 The Bimodal Models The performance of our unimodal models (DF and ASM) and our bimodal models (B-FL, P-FL, and C-FL) is shown in Figure 5. As we can see, our disfluency feature model outperforms our ASM visual model on all emotion dimensions. 168). Recall that there are only 6 disfluency features, while there are 2310 ASM visual features.

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