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Defeating the homogeneity assumption

De Roeck, Anne; Sarkar, Avik and Garthwaite, Paul H. (2004). Defeating the homogeneity assumption. In: Seventh International Conference on the Statistical Analysis of Textual Data (JADT) (Purnelle, G.; Fairo, C and Dister, A eds.), UCL Presses Universitaries, Louvain, pp. 282–294.

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The statistical NLP and IR literatures tend to make a “homogeneity assumption” about the distribution of terms, either by adopting a “bag of words” model, or in their treatment of function words. In this paper we develop a notion of homogeneity detection to a level of statistical significance, and conduct a series of experiments on different datasets, to show that the homogeneity assumption does not generally hold. We show that it also does not hold for function words. Importantly, datasets and document collections are found not to be neutral with respect to the property of homogeneity, even for function words. The homogeneity assumption is defeated substantially even for collections known to contain similar documents, and more drastically for diverse collections. We conclude that it is statistically unreasonable to assume that word distribution within a corpus is homogeneous. Because homogeneity findings differ substantially between different collections, we argue for the use of homogeneity measures as a means of profiling datasets.

Item Type: Conference or Workshop Item
Copyright Holders: 2004 The Authors
Keywords: homogeneity; term distribution; corpus profiling
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Research Group: Centre for Research in Computing (CRC)
Item ID: 22604
Depositing User: Sarah Frain
Date Deposited: 17 Aug 2010 11:36
Last Modified: 07 Dec 2018 09:38
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