Download Affective Computing and Intelligent Interaction: Fourth by Abe Kazemzadeh, Sungbok Lee, Panayiotis G. Georgiou, PDF

By Abe Kazemzadeh, Sungbok Lee, Panayiotis G. Georgiou, Shrikanth S. Narayanan (auth.), Sidney D’Mello, Arthur Graesser, Björn Schuller, Jean-Claude Martin (eds.)

The two-volume set LNCS 6974 and LNCS 6975 constitutes the refereed court cases of the Fourth foreign convention on Affective Computing and clever interplay, ACII 2011, held in Memphis,TN, united states, in October 2011.
The one hundred thirty five papers during this quantity set provided including three invited talks have been rigorously reviewed and chosen from 196 submissions. The papers are equipped in topical sections on reputation and synthesis of human impact, affect-sensitive purposes, methodological concerns in affective computing, affective and social robotics, affective and behavioral interfaces, proper insights from psychology, affective databases, evaluate and annotation tools.

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Extra resources for Affective Computing and Intelligent Interaction: Fourth International Conference, ACII 2011, Memphis, TN, USA, October 9–12, 2011, Proceedings, Part II

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Using interval type-2 fuzzy logic to translate emotion words from spanish to english. In: IEEE World Conference on Computational Intelligence (WCCI) FUZZ-IEEE Workshop (2010) 14. : A Mathematical Introduction to Logic, 2nd edn. Academic Press, London (2001) 15. R. ): Formal Concept Analysis: foundation and applications. Springer, Berlin (2005) 16. : The slashdot zoo: Mining a social network with negative costs. In: World Wide Web Conference (WWW 2009), Madrid, pp. 741–750 (April 2009) 17. : Determining what questions to ask, with the help of spectral graph theory.

2 Parsing and Tokenization In our approach, rich feature representations are used to distinguish between emotions expressed toward different targets. In order to generate these representations, we did parts-of-speech tagging using the POS tagger developed by [6]. First we reviewed Table 1, and from the features in this table we used pronoun, noun, verb, adverb and adjective before and after emotion seeds, along with their tokens, for each emotion category. This step produced a syntactic analysis for each sentence.

We note here, with one pattern, the system can generate various emotion sentences for different emotion classes, but the type of emotion sentence to be generated is the user’s choice. As any research, our work has some limitations, as well. For example, for the results in Table 6, some sentences might not be perfect English sentences. For example, “Amy looks happy girl” must be “Amy looks a happy girl”, or, for the sentence “She is nice person”, is better to have “She is a nice person“. As we can see, these sentence will be more fluent, if the determiner “a” is added.

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