By Alexander Gelbukh
This two-volume set, inclusive of LNCS 8403 and LNCS 8404, constitutes the completely refereed court cases of the 14th foreign 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; rfile 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 acceptance; opinion mining and social networks; laptop translation and multilingualism; info retrieval; textual content class and clustering; textual content summarization; plagiarism detection; kind 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
24 percent accuracy on the Penn Treebank WSJ . 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). , around 72% sentences express opinions. Table 3 shows the class distribution of this data set. We also manually correct a few erroneous tags, for example, “can” is a noun but has been tagged as a modal auxiliary verb (garbage/NN can/MD) for 4 times; “need” is a noun but has been tagged as a modal auxiliary verb once(your/PRP need/MD ).
In: Proceedings of the 14th ACM International Conference on Multimodal Interaction, pp. 485–492. ACM (2012) 6. : Multiresolution gray-scale and rotation invariant texture classiﬁcation with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 971–987 (2002) 7. : Expression of emotion in voice and music. Journal of Voice 9, 235–248 (1995) 8. : Challenges in real-life emotion annotation and machine learning based detection. Neural Networks 18, 407–422 (2005) 9.
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. This suggests that the large visual feature set is dominating and introduces noise into the model. We can see that the PCA-FL model and the CFS-FL model perform better than the Basic-FL model in general, thus applying feature engineering to the concatenated feature set helps to reduce the inﬂuence of noisy visual features.
Computational Linguistics and Intelligent Text Processing: 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part II by Alexander Gelbukh