Helmut Prendinger

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Discourse structures have a central role in several computational tasks, such as question–answering or dialogue generation. In particular, the framework of the Rhetorical Structure Theory (RST) offers a sound formalism for hierarchical text organization. In this article, we present HILDA, an implemented discourse parser based on RST and Support Vector(More)
& In this paper, we report on our efforts in developing affective character-based interfaces, i.e., interfaces that recognize and measure affective information of the user and address user affect by employing embodied characters. In particular, we describe the Empathic Companion, an animated interface agent that accompanies the user in the setting of a(More)
In this paper, we discuss scripting tools that aim at facilitating the design of web-based interactions with animated characters capable of affective communication. Specifically, two systems are developed. The SCREAM system is a scripting tool that enables authors to create emotionally and socially appropriate responses of animated characters. Content(More)
This paper introduces a new algorithm to parse discourse within the framework of Rhetorical Structure Theory (RST). Our method is based on recent advances in the field of statistical machine learning (multivariate capabilities of Support Vector Machines) and a rich feature space. RST offers a formal framework for hierarchical text organization with strong(More)
We present a chat system that uses animateddynamic text associated with emotional information to show the affective state of the user. The system obtains the affective state of a chat user from a physiological sensor attached to the user's body. This paper describes preliminary experiments and provides examples of possible applications of our chat system.(More)
This paper presents a novel approach to Emotion Estimation that assesses the affective content from textual messages. Our main goals are to detect emotion from chat or other dialogue messages and to employ animated agents capable of the emotional reasoning based on the textual interaction. In this paper, the emotion estimation module is applied to a chat(More)
The main task we address in our research is classification of text using fine-grained attitude labels. The developed @AM system relies on the compositionality principle and a novel approach based on the rules elaborated for semantically distinct verb classes. The evaluation of our method on 1000 sentences, that describe personal experiences, showed(More)
In this paper, we address the tasks of recognition and interpretation of affect communicated through text messaging. The evolving nature of language in online conversations is a main issue in affect sensing from this media type, since sentence parsing might fail while syntactical structure analysis. The developed Affect Analysis Model was designed to handle(More)