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By Alexander Gelbukh

This two-volume set, along with LNCS 8403 and LNCS 8404, constitutes the completely refereed lawsuits 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 provided including four invited papers have been rigorously reviewed and chosen from three hundred submissions. The papers are equipped within the following topical sections: lexical assets; record illustration; morphology, POS-tagging, and named entity attractiveness; syntax and parsing; anaphora solution; spotting textual entailment; semantics and discourse; normal language iteration; sentiment research and emotion attractiveness; opinion mining and social networks; laptop translation and multilingualism; details retrieval; textual content category and clustering; textual content summarization; plagiarism detection; variety and spelling checking; speech processing; and applications.

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Extra info for Computational Linguistics and Intelligent Text Processing: 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part II

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Dynamic) Therefore, we cannot simply rely on the existence of a certain auxiliary verb to determine the sentiment orientation of a sentence. We must also take the contextual information into account, which motivates the proposal of the features used in classification. 5 Feature Construction We take a supervised learning approach to classifying the sentiment orientation of sentences. , general linguistic features, and features that are directly related to modality analysis. 1 General Linguistic Features Some general features, such as opinion words, and negation words, have proved useful in sentiment analysis of all kinds of sentences.

Deontic modality recognized by this rule is used to give advice to the audience “you”. For example, the sentence below is a deontic modality matching this rule. You should buy an iphone4 . ↓ ↓ ↓ ↓ ↓ ↓ VB DT NN . PRP MD 10 Y. Liu et al. (e) I will(would) + VB This rule is used to recognize dynamic modality, with which the speaker expresses his/her willingness to do something. The subject of this type of sentence is “I”, and the modality auxiliary word is “will” or “would”. For example, the sentence below is recognized as dynamic modality using this rule.

This bimodal model only outperforms the worse performing unimodal model (the visual model). , Principal Components Analysis (PCA) and Correlation-based Feature-subset Selection (CFS)), to the concatenated feature set may improve the performance of the bimodal model by reducing the drawbacks of the less predictive features and increasing the benefits given by the more predictive features. To sum up, in this work, we test the following three hypotheses: 1. Using high-level features will improve the performance of emotion recognition compared to using low-level features.

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