This paper considers the feature selection scenario where only a few features are accessible at any time point. For example, features are generated sequentially and visible one by one. Therefore, one has to make an online decision to identify key features after all features are only scanned once or twice. The optimization based approach is a powerful tool for the online feature selection.

To build up spontaneous conversation, it is important to select topics without a feeling of strangeness. When someone notices others are not interested in a topic, he/she tries to find a new topic. Then, he/she thinks of viewpoints of the conversation and selects a topic associated with the current topic from the viewpoints. To automate viewpoint-based topic selection, we present 5W viewpoint associative topic search. The method estimates the weights of 5W viewpoints (who, what, where, when and why) from conversation, to use an appropriate similarity to search for the next topic.

One of the subjects in knowledge management is knowledge externalization and combination in which people express their tacit knowledge and formulate it. Communication among people is important for this process. This paper proposes a model that helps two users to have a discussion for knowledge externalization and combination. In this model, the computer verifies the consistency between the knowledge generated by inductive learning from cases and the two user's knowledge sets expressed by themselves. From the result of this verification, it provides the point at issue for the discussion to improve the knowledge. We conducted a user experiment in the domain of gure-fishing where real experts had participated in. We confirmed the effectiveness in activating people's communication and also in improving the quality of their knowledge.

Buyers in online auctions write feedback comments to the sellers from whom the buyers have bought the items. Other bidders read them to determine which item to bid for. In this research, we aim at helping bidders by summarizing the feedback comments. Firstly, we examine feedback comments in online auctions. From the results of the examination, we propose a method called social summarization method, which uses social relationships in online auctions for summarizing feedback comments. We implement a system based on our method and evaluate its effectiveness. Finally, we propose an interactive presentation method of the summaries based on the result of the evaluation.

Net auctions have been widely utilized with the recent development of the Internet. However it has a problem that there are too many items for bidders to select the most suitable one. We aim at supporting bidders on net auctions by automatically extracting the information of the item's features from Web pages in net auctions and generating a table containing the features of some items for comparison. But because descriptions are not uniform in net auctions, there are two problems in extracting the features. The first problem is that there are some formats. The second problem is that the keywords of features are sometimes omitted. We proposed the solutions to the problems. The solution to the first problem is to distinguish the format type from tables, items and sentences, and extract the feature values in the most suitable way. The solution to the second problem is to learn the keywords in extracting from the descriptions with the keywords. And after that, the keywords are used in extracting from the descriptions without keywords. And we constructed the system which collects the information of items, extracts their features from their text information by text mining methods and generates the table containing extracted features.