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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/9211

Title: Augmented Mutual Information for Multi-word Extraction
Authors: Zhang, Wen
Yoshida, Taketoshi
Ho, Tu Bao
Tang, Xijin
Keywords: Multi-word extraction
Mutual information
Augmented mutual information
Word dependency
Issue Date: 2009-02
Publisher: ICIC International
Magazine name: International Journal of Innovative Computing, Information and Control
Volume: 5
Number: 2
Start page: 543
End page: 554
Abstract: In order to extract multi-words from documents, mutual information (MI), as a statistical method, is the most popular solution under consideration. However, there are two kinds of deficiencies inherent in MI. One is the problem of unilateral cooccurrence, and the other is rare occurrence problem. To attack these two problems, augmented mutual information (AMI) is proposed in this paper to measure word dependency for multi-word extraction. We prove theoretically that AMI has the capacity to approximate MI to capture the independency of individual words, but it will amplify the significance of dependent individual words which may be possible multi-words. And ourexperimental results on Chinese multi-word extraction demonstrate that AMI method hassuperior performance to traditional MI method.
Rights: Copyright (C) 2009 ICIC International. Wen Zhang, Taketoshi Yoshida, Tu Bao Ho and Xijin Tang, International Journal of Innovative Computing, Information and Control, 5(2), 2009, 543-554.
URI: http://hdl.handle.net/10119/9211
Material Type: publisher
Appears in Collections:a10-1. 雑誌掲載論文 (Journal Articles)

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