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.

Information retrieval (IR) systems are used for finding, within a large text database, those documents containing information needed by a user. The complex and poorly understood semantics of documents and user queries has made feedback and adaptation important characteristics of IR systems. In this paper we briefly survey previous research on machine learning in IR systems and discuss promising areas for future research at the intersection of these two fields. 1 Introduction The goal of information retrieval (IR) techniques is to find, within a large database of documents, those documents which satisfy a user information need. Typically the stored documents are composed of natural language text, though IR techniques have also been applied to databases of stored speech, images, computer source code, and other forms of information. In contrast to conventional database techniques, IR techniques are most useful when the semantics of the objects to be retrieved is unclear, and the relation...

Since user's relevance judgments are a source of evidence for information retrieval, learning from this feedback is an appealing idea. Many different learning techniques have successfully been used for relevance feedback. In most models, learning is either performed off line or is based on simple heuristics. The approach we propose is based on a classical probabilistic model in which learning from feedback is simple and incremental. In this paper, we extend this model, presenting a new similarity function that takes into account feedback on the entire database while computing the score between a query and a single document. As a result, when a user judges a document it modifies the whole retrieval process.