An Evaluation Of Phrasal And Clustered Representations On A Text Categorization Task

Abstract

Syntactic phrase indexing and term clustering have been widely explored as text representation techniques for text retrieval. In this paper we study the properties of phrasal and clustered indexing languages on a text categorization task, enabling us to study their properties in isolation from query interpretation issues. We show that optimal effectiveness occurs when using only a small proportion of the indexing terms available, and that effectiveness peaks at a higher feature set size and lower effectiveness level for a syntactic phrase indexing than for word-based indexing. We also present results suggesting that traditional term clustering method are unlikely to provide significantly improved text representations. An improved probabilistic text categorization method is also presented.