Social Network Services(SNSs), which are maintained by a community of people, are among the popular Web 2.0 tools. Multiple users freely post their comments to an SNS thread. It is difficult to understand the gist of the comments because the dialog in an SNS thread is complicated. In this paper, we propose a system that presents the gist of information at a glance and basic information about an SNS thread by using Wikipedia. We focus on the table of contents (TOC) of the relevant articles on Wikipedia. Our system compares the comments in a thread with the information in the TOC and identifies contents that are similar. We consider the similar contents in the TOC as the gist of the thread and paragraphs in Wikipedia similar to the comments in the thread as comprising basic information about the thread. Thus, a user can obtain the gist of an SNS thread by viewing a table with similar contents.

SNSs and blogs, both of which are maintained by a community of people, have become popular in Web 2.0. We call these content as "Community-type content." This community is associated with the content, and those who use or contribute to community-type content are considered as members of the community. Occasionally, the members of a community do not understand the theme of the content from multiple viewpoints, hence, the amount of information is often insufficient. It is convenient to present the user missed information. In this way, when Web 2.0 became popular, the content on the Internet and type of users are changed. We believe that there is a need for next-generation search engines in Web 2.0. We require a search engine that can search for information users are unaware of; we call such information as "content holes." In this paper, we propose a method for searching content holes in community-type content. We attempt to extract and represent content holes from discussions on SNSs and blogs. Conventional Web search technique is generally based on similarities. On the other hand, our content-hole search is a different search. In this paper, we classify and represent a number of images for different searching methods; we define content holes and as the first step toward realizing our aim, we propose a content-hole search system using Wikipedia.

In community-type content such as blogs and SNSs, we call the user's unawareness of information as a "content hole" and the search for this information as a "content hole search." A content hole search differs from similarity searching and has a variety of types. In this paper, we propose different types of content holes and define each type. We also propose an analysis of dialogue related to community-type content and introduce content hole search by using Wikipedia as an example.

In this paper, we present a method that improves Japanese dependency parsing by using large-scale statistical information. It takes into account two kinds of information not considered in previous statistical (machine learning based) parsing methods: information about dependency relations among the case elements of a verb, and information about co-occurrence relations between a verb and its case element. This information can be collected from the results of automatic dependency parsing of large-scale corpora. The results of an experiment in which our method was used to rerank the results obtained using an existing machine learning based parsing method showed that our method can improve the accuracy of the results obtained using the existing method.