NDLI: A Fast Approximate AIB Algorithm for Distributional Word Clustering

Content Provider	IEEE Xplore Digital Library
Author	Lei Wang Jianjia Zhang Luping Zhou Wanqing Li
Copyright Year	2013
Description	Author affiliation: Sch. of Comput. Sci. & Software Eng., Univ. of Wollongong, Wollongong, NSW, Australia (Lei Wang; Jianjia Zhang; Luping Zhou; Wanqing Li)
Abstract	Distributional word clustering merges the words having similar probability distributions to attain reliable parameter estimation, compact classification models and even better classification performance. Agglomerative Information Bottleneck (AIB) is one of the typical word clustering algorithms and has been applied to both traditional text classification and recent image recognition. Although enjoying theoretical elegance, AIB has one main issue on its computational efficiency, especially when clustering a large number of words. Different from existing solutions to this issue, we analyze the characteristics of its objective function-the loss of mutual information, and show that by merely using the ratio of word-class joint probabilities of each word, good candidate word pairs for merging can be easily identified. Based on this finding, we propose a fast approximate AIB algorithm and show that it can significantly improve the computational efficiency of AIB while well maintaining or even slightly increasing its classification performance. Experimental study on both text and image classification benchmark data sets shows that our algorithm can achieve more than 100 times speedup on large real data sets over the state-of-the-art method.
Starting Page	556
Ending Page	563
File Size	769547
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.78
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Merging Mutual information Approximation algorithms Joints Time complexity Barium Image Recognition Word clustering Agglomerative Information Bottleneck Visual Codebook
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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