By Alexander Gelbukh

This two-volume set, including 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 awarded 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; record illustration; morphology, POS-tagging, and named entity reputation; syntax and parsing; anaphora answer; spotting textual entailment; semantics and discourse; usual 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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Extra resources 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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The category of modality features are also used to built vectors. 1 Y. Liu et al. Experimental Evaluation Data Sets We carry out the experiments using customer reviews of four categories of products: vacuum, cellphone, haircare and mattress. There are 9,152 sentences in total. 24 percent accuracy on the Penn Treebank WSJ [21]. Using the POS tag information, we extract 1,635 sentences from the data set, each of which contains at least one modal verb. We also annotate their sentiment orientation (positive, negative or neutral) manually in the same way as described in (Hu and Liu, 2004).

Fig. 6. Comparing our bimodal model with the AVEC2012 WLSC results 4 Discussion Based on the experimental results, we verified our main hypothesis that using high-level features, namely the disfluency features and the ASM features, improves model performance compared to using low-level features. The average performance of our disfluency model using only the 6 disfluency features is not significantly different from the best audio-visual model in AVEC2012 WLSC. This indicates that disfluency features are especially powerful in emotion prediction.

I wish it were a little lighter . ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ PRP VB PRP VBD DT RB JJR . (d) You should(may) + VB This rule is used to recognize deontic modality, with which the speaker tries to get others to do something. 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.

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