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KDD
2008
ACM

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

14 years 11 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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