Text classification using lattice machine

Abstract

A novel approach to supervised learning, called Lattice Machine, was proposed in [5]. In the Lattice Machine, it was assumed that data are structured as relations. In this paper we investigate the application of the Lattice Machine in the area of text classification, where textual data are unstructured. We represent a set of textual documents as a collection of Boolean feature vectors, where each vector corresponds to one document and each entry in a tuple indicates whether a particular term appears in the document. This is a common representation of textual documents. We show that using this representation, the Lattice Machine’s operations are simply set theoretic operations. In particular, the lattice sum operation is simply set intersection and the ordering relationship is simply set inclusion. Experiments show that the Lattice Machine, under this configuration, is quite competitive with state-of-the-art learning algorithms for text classification.

The authors gratefully acknowledge support by the KBN/British Council Grant No WAR/992/151