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.

Text categorization is the classification of documents with respect to a set of predefined categories. In this paper, we propose a new probabilistic model for text categorization, that is based on a Single random Variable with Multiple Values (SVMV). Compared to previous probabilistic models, our model has the following advantages; 1) it considers within-document term frequencies, 2) considers term weighting for target documents, and 3) is less affected by having insufficient training cases. We verify our model's superiority over the others in the task of categorizing news articles from the "Wall Street Journal".

Clear and precise queries are a necessity when searching very large document collections, especially when query-based retrieval is the only means of exploration. We propose system-mediated information access as a solution for users ’ well-documented inability to formulate good queries. Our approach is based on two main assumptions: first, on the ability of document clustering to reveal the topical, semantic structure of a problem domain represented by a specialized “source collection,” and, second, on the capacity of statistical language models to convey content. Taking the role of the human mediator or intermediary searcher, a mediation system interacts with the user and supports her exploration of a relatively small source collection, chosen to be representative for the problem domain. Based on the

Text categorization is the classification of documents with respect to a set of predefined categories. In this paper, we propose a new probabilistic model for text categorization, that is based on a Single random Variable with Multiple Values (SVMV). Compared to previous probabilistic models, our model has the following advantages; 1) it considers within-document term frequencies, 2) considers term weighting for target documents, and 3) is less affected by having insufficient training cases. We verify our model's superiority over the others in the task of categorizing news articles from the "Wall Street Journal". 1 Introduction Text categorization is the classification of documents with respect to a set of predefined categories. As an example, let us take a look at the following article from the "Wall Street Journal" (1989/11/2). McDermott International, Inc. said its Babcock & Wilcox unit completed the sale of its Bailey Controls Operations to Finmeccanica S.p.A for $295 million. Fin...