Automatic thesaurus generation for an electronic community system
Journal of the American Society for Information Science
A cooccurrence-based thesaurus and two applications to information retrieval
Information Processing and Management: an International Journal
Word classification and hierarchy using co-occurrence word information
Information Processing and Management: an International Journal
Joining automatic query expansion based on thesaurus and word sense disambiguation using WordNet
International Journal of Computer Applications in Technology
Using Wikipedia knowledge to improve text classification
Knowledge and Information Systems
Automatic thesaurus construction for spam filtering using revised back propagation neural network
Expert Systems with Applications: An International Journal
Thesaurus enrichment for query expansion in audiovisual archives
Multimedia Tools and Applications
Context constraint disambiguation of word semantics by field association schemes
Information Processing and Management: an International Journal
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A thesaurus is one of important knowledge in natural language processing and is manually made in general. However, as growth of the scale, frequent update is difficult because it takes huge time by hand. This paper aims to construct a hierarchical large-scale thesaurus by a clustering scheme based on co-occurrence information among words. In the proposed clustering algorithm, the Kullback-Leibler divergence is introduced as a similarity measurement in order to judge superordinate and subordinate relations. Besides, the thesaurus tree can be incrementally updated in each node for a minute change such as the addition of unknown words. In order to evaluate the presented method, a thesaurus consisting of about 60,000 words is made by using about 16 million co-occurrence relationships extracted from the Google N-gram. From random data in the thesaurus, it turns out that the proposed method for a large-scale thesaurus achieves high precision of 0.826.