Term clustering and syntactic phrase formation are methods for transforming natural language text. Both have had only mixed success as strategies for improving the quality of text representations for document retrieval. Since the strengths of these methods are complementary, we have explored combining them to produce superior representations. In this paper we discuss our implementation of a syntactic phrase generator, as well as our preliminary experiments with producing phrase clusters. These experiments show small improvements in retrieval effectiveness resulting from the use of phrase clusters, but it is clear that corpora much larger than standard information retrieval test collections will be required to thoroughly evaluate the use of this technique.

The way in which text is represented has a strong impact on the performance of text classification (retrieval and categorization) systems. We discuss the operation of text classification systems, introduce a theoretical model of how text representation impacts their performance, and describe how the performance of text classification systems is evaluated. We then present the results of an experiment on improving text representation quality, as well as an analysis of the results and the directions they suggest for future research. 1 The Task of Text Classification Text-based systems can be broadly classified into classification systems and comprehension systems. Text classification systems include traditional information retrieval (IR) systems, which retrieve texts in response to a user query, as well as categorization systems, which assign texts to one or more of a fixed set of categories. Text comprehension systems go beyond classification to transform text in some way, such as prod...