Statistical Language Learning
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Automatic noun classification by using Japanese-English word pairs
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Noun classification from predicate-argument structures
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Information retrieval using robust natural language processing
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Text analysis and knowledge mining system
IBM Systems Journal
Synonymous collocation extraction using translation information
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
The talent system: TEXTRACT architecture and data model
SEALTS '03 Proceedings of the HLT-NAACL 2003 workshop on Software engineering and architecture of language technology systems - Volume 8
Gram-free synonym extraction via suffix arrays
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Generating phrasal and sentential paraphrases: A survey of data-driven methods
Computational Linguistics
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We present a text mining method for finding synonymous expressions based on the distributional hypothesis in a set of coherent corpora. This paper proposes a new methodology to improve the accuracy of a term aggregation system using each author's text as a coherent corpus. Our approach is based on the idea that one person tends to use one expression for one meaning. According to our assumption, most of the words with similar context features in each author's corpus tend not to be synonymous expressions. Our proposed method improves the accuracy of our term aggregation system, showing that our approach is successful.