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2008
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Knowledge discovery of semantic relationships between words using nonparametric bayesian graph model

9 years 3 months ago
Knowledge discovery of semantic relationships between words using nonparametric bayesian graph model
We developed a model based on nonparametric Bayesian modeling for automatic discovery of semantic relationships between words taken from a corpus. It is aimed at discovering semantic knowledge about words in particular domains, which has become increasingly important with the growing use of text mining, information retrieval, and speech recognition. The subject-predicate structure is taken as a syntactic structure with the noun as the subject and the verb as the predicate. This structure is regarded as a graph structure. The generation of this graph can be modeled using the hierarchical Dirichlet process and the Pitman-Yor process. The probabilistic generative model we developed for this graph structure consists of subject-predicate structures extracted from a corpus. Evaluation of this model by measuring the performance of graph clustering based on WordNet similarities demonstrated that it outperforms other baseline models. Categories and Subject Descriptors G.3 [Probability and Stat...
Issei Sato, Minoru Yoshida, Hiroshi Nakagawa
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2008
Where KDD
Authors Issei Sato, Minoru Yoshida, Hiroshi Nakagawa
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