Abstract
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.
