We have developed a visualization system for dialog text exchanged in e-learning virtual classrooms. In this system, text-based online discussions among learners are effectively visualized as discussions held in a virtual classroom in cyberspace. Discussion participants are displayed as avatars. The virtual classroom maintains the interest of learners because it incorporates professional camerawork and switching know-how based on rules derived from an analysis of 42 TV programs. The gestures of the CG avatar depend on the dialog text. A series of virtual classroom experiments confirmed that elementary and junior high school students maintained an interest in using the system.

We describe an online method for selecting and annotating highlight scenes in soccer matches being televised. The stadium crowd noise and the play-by-play announcer's voice are used as input signals. Candidate scenes for highlights are extracted from the crowd noise by dynamic thresholding and spectral envelope analysis. Using a dynamic threshold solves the problem in conventional methods of how to determine an appropriate threshold. Semantic-meaning information about the kind of play and the related team and player is extracted from the announcer's commentary by using domain-based rules. The information extracted from the two types of audio input is integrated to generate segment-metadata of highlight scenes. Application of the method to six professional soccer games has confirmed its effectiveness.

This paper presents a method to automatically acquire a given noun's telic and agentive roles from corpus data. These relations form part of the qualia structure assumed in the generative lexicon, where the telic role represents a typical purpose of the entity and the agentive role represents the origin of the entity. Our proposed method employs a supervised machine-learning technique which makes use of template-based contextual features derived from token instances of each noun. The output of our method is a ranked list of verbs for each noun, across the different qualia roles. We also propose a variant of Spearman's rank correlation to evaluate the correlation of two top-N ranked lists. Using this correlation method, we represent the ability of the proposed method to identify qualia structure relative to a conventional template-based method.