Masaki Eto

Public libraries are increasingly offering health and medical information services as part of their problem-solving support. This study investigated the collections and usage statuses of libraries in four prefectures to examine how effectively they provide citizens with access to books on health and medical information. Specifically, we conducted a random sample of 10,000 books from the national bibliography and explored the relationship between book collection, usage, and the selection tools employed by libraries in Chiba, Kanagawa, Wakayama, and Kochi Prefectures. The findings revealed that the number of books available for lending increased substantially when libraries of different types were incorporated into an interlibrary loan network. In addition, prefectural libraries and libraries of other types demonstrated a tendency to maintain collections of high-quality books. Our results also indicate that materials with more recent publication dates were borrowed more frequently and high-quality books were circulated relatively often. It was found that prefectural libraries play a pivotal role in providing citizens with access to books, such as providing reliable books within the prefecture and building a system for interlibrary loans.

The purpose of this research is to assign automatically appropriate keywords to the reference records on the basis of the sentences of questions and answers in the reference records. Former researches extracted appropriate words automatically as keywords from the reference records, but this method could assign only the words in the texts. This research proposes a method of assigning of keywords using titles and snippets of web pages. The method consists of three processes: (1) the process of extracting keywords as search terms from the reference records by the machine learning, (2) the process of acquiring titles and snippets from Google, and (3) the process of acquiring the keywords by estimation of weights. The result showed that the keywords were assigned successfully with precision ratio of 31.4% and recall ratio of 61.4%.

In this paper the author proposes a sophisticated document retrieval method using context based co-citation relationship. To evaluate the effectiveness of the proposed method, two experiments were conducted. The first experiment is to compare documents retrieved by word with the ones retrieved by co-citation relationship. The second one is to compare the proposed method with the traditional method by using binary co-citation relationship based on two typical metrics (MAP and nDCG), and by analyzing documents ranked top 10. The experiments showed that the proposed method will (1) retrieve relevant documents which cannot be retrieved by using word, (2) rank retrieved relevant documents more adequately, and (3) rank relevant documents highly, even though the number of the frequencies of their co-citation is few.

In recent years Kansei retrieval systems have been developed to be practical use. We have developed the retrieval system using Kansei parameters based on online book reviews of children's books. The relation between parameters assigned manually and the words in online book-reviews has yet to be analyzed enough, however. Moreover, how to set appropriate value of Kansei parameters based on the words in online book reviews has not been theoretically proved. To resolve such problem, we have conducted an experiment. In this experiment, Kansei parameters which are consistent with the value gained manually were assigned based on "the words, which represent human feelings, impressions or atmosphere of books" using the machine learning approach. We have used 1605 book reviews to assign the parameters automatically. The result showed that : 1)using the words in book reviews is useful to assign parameters automatically, 2)using about 30 frequent words is more effective than using all the words in book reviews, and 3) the recall ratio is not influenced by the parts of speech of the words extracted from book reviews.

The purpose of this research is to extract appropriate words automatically as keywords of the reference records from the sentences of questions and answers in the reference records. The automatic extraction method consists of two processes: (1) the process of extracting candidate words from given texts, and (2) the process of judging whether each extracted candidate is appropriate or not as a keyword. We focus on the process of (2) using the machine learning approach. In this method as a training data, we have used 42 features such as where each candidate word was, how many times it appeared in the records, and the postpositional particles surrounding the candidate. We then conducted an evaluation experiment using 9,375 words judged to be keywords manually in 507 reference records of Japanese history. The result showed that the keywords were assigned successfully with precision ratio of 56.9% and recall ratio of 48.3%

In this research, we conducted the experiment of automatically classifying the reference records in the Collaborative Reference Database of the National Diet Library into Nippon Decimal Classification (NDC). Three machine learning methods: decision tree, Naïve Bayes, and Support Vector Machine (SVM) methods - were tested on the words included in the sentences of questions and answers in 634 reference records. The results indicate that the using the SMV method and answer sentences were classified successfully compared to others. As for the recall and precision ratio, there was small difference among the methods, and between question and answer sentences. The significant difference, however, was observed in the recall ratio by the NDC category; the result of some category obtained more than 60% recall ratio, which suggested that classifying reference records into NDC by using machine learning methods was effective.

Co-citation, which is a typical similarity indicator among papers, has a premise that the degrees of similarity among cited papers are equal in one citing paper. But the similarity strength can be guessed from the distance between the places where cited papers are shown in a citing paper. In this paper, three distance measures ("physical distance", "co-occurrence of citing words", and "structural distance") are introduced for the purpose and these measures are evaluated.