Computational Linguistics and Intelligent Text Processing: by Alexander Gelbukh

By Alexander Gelbukh

This two-volume set, which include LNCS 8403 and LNCS 8404, constitutes the completely refereed lawsuits 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 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; rfile illustration; morphology, POS-tagging, and named entity reputation; syntax and parsing; anaphora solution; spotting textual entailment; semantics and discourse; traditional language new release; sentiment research and emotion popularity; opinion mining and social networks; laptop translation and multilingualism; info retrieval; textual content class 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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Speaker normalization is applied after grouping the data by speaker. 5 Modality Fusion and Feature Engineering In our work, we applied a FL modality fusion method and concatenated our disfluency and ASM visual feature sets into one set. Simple concatenation without any further feature engineering is referred to as Basic-FL in the following. The feature set used by our Basic-FL model contains 2316 features. We also study the influence of two feature engineering methods on the BasicFL model. The first method is PCA, which maps the original features to a lower dimensional space, thus reducing the size and redundancy of the feature set.

Moore, L. Tian, and C. Lai varied from daily life to political issues. The 24x4 recordings are divided into training set, development set, and test set, each of which contains 32 dialogue sessions. In the AVEC database, subjects in the test set are different people from those in the training and development sets. For the WLSC, each word spoken by a subject is a data instance. The number of instances contained in the training set, development set, and test set are 20169, 16300, and 13405, respectively.

Challenges in real-life emotion annotation and machine learning based detection. Neural Networks 18, 407–422 (2005) 9. : A multimodal fuzzy inference system using a continuous facial expression representation for emotion detection. In: Proceedings of the 14th ACM International Conference on Multimodal Interaction, pp. 493–500. ACM (2012) 10. : Degree of human perception of facial emotions based on audio and video information. IEICE Technical Report. Image Engineering 96, 9–15 (1996) 11. : Multimodal human emotion/expression recognition.

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