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.

This paper describes methods in which natural language is used to describe video contents, knowledge of which is needed for intelligent video manipulation. The content encoded by natural language is extracted by a language analyzer in the form of subject-centered dependency structures which is a language-oriented structure, and is combined in an incremental way into a single structure called a multi-path index tree. Content descriptors and their inter-relations are extracted from the index tree in order to provide a high speed retrieval and flexibility. The content-based video index is represented in a two-dimensional structure where in the descriptors are mapped onto a component axis and temporal references (i.e., video segments aligned to the descriptors) are mapped onto a time axis. We implemented an experimental image retrieval systems to illustrate the proposed index structure 1) has superior retrieval capabilities compare to those used in conventional methods, 2) can be generated by an automated procedure, and 3) has a compact and flexible structure that is easily expandable, making an integration with vision processing possible.

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.

In this paper, we consider the partial gathering problem of mobile agents in arbitrary networks. The partial gathering problem is a generalization of the (well-investigated) total gathering problem, which requires that all the agents meet at the same node. The partial gathering problem requires, for a given positive integer g, that each agent should move to a node and terminate so that at least g agents should meet at each of the nodes they terminate at. The requirement for the partial gathering problem is no stronger than that for the total gathering problem, and thus, we clarify the difference on the move complexity between them. First, we show that agents require Ω(gn+m) total moves to solve the partial gathering problem, where n is the number of nodes and m is the number of communication links. Next, we propose a deterministic algorithm to solve the partial gathering problem in O(gn+m) total moves, which is asymptotically optimal in terms of total moves. Note that, it is known that agents require Ω(kn+m) total moves to solve the total gathering problem in arbitrary networks, where k is the number of agents. Thus, our result shows that the partial gathering problem is solvable with strictly fewer total moves compared to the total gathering problem in arbitrary networks.