Text Categorization via Similarity Search: An Efficient and Effective Novel Algorithm

TL;DR

This paper introduces a novel supervised text categorization algorithm based on similarity search in measure distribution space, achieving high performance in competitions and on the Reuters dataset.

Contribution

It proposes a new approach using measure distribution similarity for text classification, differing from traditional centroid-based methods.

Findings

Achieved 2nd place in CDMC'2012 text categorization division.

Performed effectively on the Reuters 21578 dataset.

Efficient in both training and classification stages.

Abstract

We present a supervised learning algorithm for text categorization which has brought the team of authors the 2nd place in the text categorization division of the 2012 Cybersecurity Data Mining Competition (CDMC'2012) and a 3rd prize overall. The algorithm is quite different from existing approaches in that it is based on similarity search in the metric space of measure distributions on the dictionary. At the preprocessing stage, given a labeled learning sample of texts, we associate to every class label (document category) a point in the space of question. Unlike it is usual in clustering, this point is not a centroid of the category but rather an outlier, a uniform measure distribution on a selection of domain-specific words. At the execution stage, an unlabeled text is assigned a text category as defined by the closest labeled neighbour to the point representing the frequency distribution of the words in the text. The algorithm is both effective and efficient, as further confirmed by experiments on the Reuters 21578 dataset.