By Alexander Gelbukh

This two-volume set, which include 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 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 answer; spotting textual entailment; semantics and discourse; common language new release; sentiment research and emotion popularity; opinion mining and social networks; computer 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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Their lexical features were computed using Pointwise Mutual Information (PMI) values, which are measurements of the correlations between words and binarized emotion dimensions. , sparse PMI features using a 1000-dimension bag-of-words approach, and nonsparse PMI features using word counts. These were the only lexical features used in the AVEC2012. Their experiments showed that the sparse PMI features gave significantly better results than the non-sparse PMI features, and were the most predictive features.

Table 1. 1 We examined two additional types of features: silent pause features, which are calculated by finding silent gaps between word timings, and window-based PMI features. However, our experiments showed that these features are not as predictive as our disfluency features. Therefore, these features and the experiments related to them will not be further discussed in this paper. 3 The ASM Visual Features In our work, we use the horizontal and vertical positions of 77 facial landmarks as our visual features.

If p is greater (less) than n, the sentiment orientation of s will be designated as positive (negative), and neutral or mixed in the case of a tie. The opinion lexicon we use contains 4,783 negative opinion words and 2,006 positive opinion words1 . This method has some drawbacks. For example, some sentences may not contain those opinion words, and the sentient orientation of some words depends on the context. 2 Standard SVM Classifier SVM (Support Vector Machine) classifiers are widely used in some domains such as document classification and sentiment classification.

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