Ryozo Nakamura

Information filtering for the newspaper

A large quantity of information is spread out to our life, especially to our cyberspace. Not only are major newspaper companies providing a Web service, so are regional companies with their characteristic Web pages. We built an information filtering system using multi-agent for the article which a user wanted to read in the regional newspaper. Our system has two main agents. One is the ranking agent for the newspaper, the other is the feedback agent. The ranking agent ranks articles based on user keywords. The feedback agent feeds back to the user profile data based on the access frequency. We propose the ranking and feedback algorithms, and estimate these algorithms by experiments. Experiments show there is relation between filtered and read article by a chi-square test of 0.05 significance level. Also they show our system is efficient for the passage of time.

Neurological abnormalities in cognitively impaired but not demented elderly.

Objective– We determined the prevalence and clinical characteristics of elderly people with cognitively impaired but not demented (CIND) in the community. Material and methods– We examined 945 elderly residents (aged 65 years or more) in a cross‐sectional study of a standardized clinical examination in 27 randomly selected communities in the town of Mifune, a standard farming town in Japan. Results– The prevalence of CIND and dementia were 10.8% and 4.8%, respectively, with both rates increasing with age. The frequency of most neurological signs in CIND elderly was greater than that found in subjects with no cognitive impairment (NCI) and less than that in subjects with dementia. The mean ADL disability score in CIND subjects also lay between the mean values for those with NCI and those with dementia. There were no differences in lifestyles between the CIND group and the other groups. Conclusion– The results suggest that a majority of CIND elderly suffer from brain dysfunction due to chronic neurological disorders or aging itself.

Daily clustering for the electronic newspaper based on the analysis of trends

To classify newspaper articles automatically, the tf*idf method has been used to weight the words in an article. These methods are suitable for fixed databases, but cannot pick up the topic words of articles because the IDF methods give a low value for frequently occurring words. We propose the daily clustering method for electronic daily newspapers. Our method is based on the characteristics of articles and the change of contents. First, we define the weight function of words based on the position in the article and the change rate of content as time passes. Then we calculate the relation between articles, clustering value and the relation between clusters of different days. As a result of experiments, the accuracy of recall and precision rate improved by several percent compared with old methods.