MoL-2000-03: An Information Theoretic Approach to Finding Word Groups for Text Classification

MoL-2000-03: Verbeek, Sjaak (2000) An Information Theoretic Approach to Finding Word Groups for Text Classification. [Report]

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Abstract

An Information Theoretic Approach to Finding Word Groups for Text Classification Sjaak Verbeek This thesis concerns finding the `optimal' number of (non-overlapping) word groups for text classification. We present a method to select _which_ words to cluster in word groups and _how many_ such word groups to use on the basis of a set of pre-classified texts. The method involves a greedy search through the space of possible word groups. The criterion on which is navigated through this space is based on `mutual information' and is known as `Jensen Shannon divergence'. The criterion to decide _which number_ of word groups to use is based on Rissanen's MDL Principle. We present empirical results that indicate that the proposed method performs well at its task. The prediction model used is based on the Naive Bayes model and the date set used for the experiments is a subset of the `20 Newsgroup Dataset'.

Item Type: Report
Report Nr: MoL-2000-03
Series Name: Master of Logic Thesis (MoL) Series
Year: 2000
Date Deposited: 12 Oct 2016 14:38
Last Modified: 12 Oct 2016 14:38
URI: https://eprints.illc.uva.nl/id/eprint/710

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