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 conscientiously reviewed and chosen from 196 submissions. The papers are geared up in topical sections on reputation and synthesis of human have an effect on, affect-sensitive purposes, methodological concerns in affective computing, affective and social robotics, affective and behavioral interfaces, suitable insights from psychology, affective databases, review and annotation tools.

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Extra info 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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We also manually mapped the surface form of the answers to yes/no/other answers. In the future, we will explore how to automate these steps and represent fuzzyiness in the answers that are not clearly “yes” or “no”. After the manual normalization we found that there were 37 questions types that had been asked at least twice, eight questions types that were asked three Emotion Twenty Questions: Toward a Crowd-Sourced Theory of Emotions 7 Table 1. Examples of question standardization Standardized Question Examples cause(emptySet,e) can you feel the emotion without any external events is it an emotion that just pops up spontaneously (vs thing)?

Lexical choice: This task involves selecting from a set of semantically equivalent but syntactically different alternatives. Aggregation Task – Conjunction and other aggregation. , transforming (1) to (2): 1) Amy is happy. Amy is nice. 2) Amy is happy and nice. – Pronominalization and other reference. , transforming (3) to (4): 3) Amy is worried. Amy has an exam. 4) Amy is worried. She has an exam. – Introducing discourse markers. , transforming (5) to (6): 5) I just saw Amy, she was sick. 6) I just saw Amy, she was also sick.

In the table, L (Left) and R (Right) are parts-of-speech or tokens before emotion seeds, and parts-of-speech or tokens after emotion seeds, respectively. Previous research on word sense disambiguation in contextual analysis has identified several features that are good indicators of word properties. These include surrounding words and their POS tags, collocation and keywords in contexts [7]. Table 3 shows the percentages of these features for each emotion category, before and after emotion seeds in our data set.

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