A Simple Mixture Model For Unsupervised
Text Categorisation
Author(s)
F. Clérot, F. Fessant, O. Collin, O. Cappé & E. Moulines
Abstract
Automatically segmenting text corpora into thematically related groups is a
complex exploratory analysis problem. In this article, we outline our multi-stage
exploratory analysis process and investigate the performance of a simple
statistical model. After a description of this model and of its fitting procedure,
we illustrate its performance on the segmentation of a corpus of CKM-related
texts in English.
Keywords: text mining, exploratory analysis, clustering, mixture model.
1 Introduction
Clustering is a key tool in exploratory data analysis; segmenting the data into
homogeneous groups leads to a more synthetic understanding of the data, allows
to build powerful visualisations and is often the first step towards more specific
analysis such as supervised classification.
Although less standard in analysis of text data, clustering has recently
received a lot of attention. The goal is to bring to text data analysis the same
benefits as above.
There are however significant differences between text data analysis and
numerical data analysis: for numerical data analysis, the cluster "homogeneity" is
judged from a metric in data space; when dealing with text data, it is clearly
implicit that "homogeneity" means "topical homogeneity", a notion which is
more difficult to measure.
In this article, the purpose of text clustering is to build a topical
segmentation of a corpus. Because of the difficulty of defining a priori a topical
homogeneity measure, the text clustering analysis must be considered as a part of
Keywords
text mining, exploratory analysis, clustering, mixture model.
Related Book
Other papers in this volume
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