Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
ICSC '07 Proceedings of the International Conference on Semantic Computing
Computational models of inductive reasoning using a statistical analysis of a Japanese corpus
Cognitive Systems Research
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The purpose of this study is to construct a probabilistic hierarchical structure of categories based on a statistical analysis of Japanese corpus data and to verify the validity of the structure by conducting a psychological experiment. At first, the co-occurrence frequencies of adjectives and nouns within modification relations were extracted from a Japanese corpus. Secondly, a probabilistic hierarchical structure was constructed based on the probability, P(category|noun), representing the category membership of the nouns, and utilizing categorization information in a thesaurus and a soft clustering method (Rose's method [1]) with co-occurrence frequencies as initial values. This method makes it possible to identify the constructed hierarchical structure. In order to examine the validity of the constructed hierarchy, a psychological experiment was conducted. The results of the experiment verified the psychological validity of the hierarchical structure.