出版社：The Japanese Society for Artificial Intelligence
摘要：Identification of language correspondences between two different sets of language data, which is individually provided by different researchers, is one of the main problems that should be addressed in the research of the world's languages matching. It will be effective for identifying the same language if a language code is assigned to any language as a unique identifier, but such assignment is not usually available for most cases. A method proposed by Wu and Matsuno enabled this identification by using two measures of language name similarity and language classification similarity, and having succeeded in searching 88% languages included in one set of language data that relate to another set of language data. The aim of this paper is to improve the accuracy of this identification by taking into account brother information in a language classification tree. After giving an overview of the method by Wu and Matsuno, we point out the problem that language name similarity and language classification similarity are not utilized effectively, that is, their method gave an inappropriate decision even if either of these two similarities has a complete matching. To address this problem, we define two kinds of new measures: one is a similarity of languages based on brother information, and the other is a language general similarity that integrates the similarities of language name and language classification. Our experimental result shows that our new method is more effective than the previous one.
关键词：language name similarity ; language classification similarity ; language general similarity ; family tree ; string similarity
