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 lawsuits of the Fourth overseas convention on Affective Computing and clever interplay, ACII 2011, held in Memphis,TN, united states, in October 2011.
The a hundred thirty five papers during this quantity set offered including three invited talks have been conscientiously reviewed and chosen from 196 submissions. The papers are equipped in topical sections on reputation and synthesis of human impact, affect-sensitive functions, methodological matters in affective computing, affective and social robotics, affective and behavioral interfaces, appropriate insights from psychology, affective databases, assessment and annotation tools.

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Additional 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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One drawback of this approach is that it will hard to quantify reliability in the unconstrained interactions of EMO20Q. Reliability can be measured in the amount of agreement between subjects, but this can be difficult because we do not force subjects to pick any particular words, so the words that are in common between users are determined by chance and hence sparse. Because of this, an alternative way to measure reliability could be done in offline question-answering of specific question-emotion pairs, which could be repeated across subjects.

Stud. 59, 157–183 (2003) 11. : Evidence for a three-factor theory of emotions. Journal of Research in Personality 11, 273–294 (1977) 12. : An interval type-2 fuzzy logic system to translate between emotion-related vocabularies. In: Proceedings of Interspeech, Brisbane, Australia (September 2008) 13. : 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.

Keshtkar and D. Inkpen Fig. 1. Our Pattern-based Architecture for Sentence Generation 2 Our Model to Generate Emotion Sentences The vast amount of text that is becoming available online offers new possibilities for achieving corpus-based approaches to NLG systems. We have developed a ’pattern-based model’, a system that generates extraction patterns using untagged text. Our model requires only a pre-classified corpus of relevant and irrelevant texts; nothing inside the texts needs to be tagged in any way.

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