Hierarchical word clustering — automatic thesaurus generation

Hierarchical Latent Word Clustering

This paper presents a new Bayesian non-parametric model by extending the usage of Hierarchical Latent Dirichlet Allocation to extract tree structured word clusters from text data. The inference algorithm of the model collects words in a cluster if they share similar distribution over documents. In our experiments, we observed meaningful hierarchical structures on NIPS corpus and radiology repor...

Automatic Thesaurus Generation using Co-occurrence

This paper proposes a characterization of useful thesaurus terms by the informativity of cooccurence with that term. Given a corpus of documents, informativity is formalized as the information gain of the weighted average term distribution of all documents containing that term. While the resulting algorithm for thesaurus generation is unsupervised, we find that high informativity terms correspo...

Automatic Thesaurus Generation through Multiple Filtering

11, this paper, we propose a method of gen(',rating bilingual keyword eh.lsters or thesauri from parallel or comi.m, able bilingual corpora. The method combines nmrphological and lexical processing, bilingual word aligmnent, and graph-theoretic cluster generation. An experiment shows that the method is promising. 1 I n t r o d u c t i o n In this paper, we propose a method of automatte bilingua...

Hierarchical Word Clustering Forrelevance Judgements

Fuzzy Clustering Approach Using Data Fusion Theory and its Application To Automatic Isolated Word Recognition

 In this paper, utilization of clustering algorithms for data fusion in decision level is proposed. The results of automatic isolated word recognition, which are derived from speech spectrograph and Linear Predictive Coding (LPC) analysis, are combined with each other by using fuzzy clustering algorithms, especially fuzzy k-means and fuzzy vector quantization. Experimental results show that the...