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In this paper, we compare learning techniques based on statistical classification to traditional methods of relevance feedback for the document routing problem. We consider three classification techniques which have decision rules that are derived via explicit error minimization linear discriminant analysis, logistic regression, and neuraf networks. We demonstrate that the classifiers perform 10-15% better than relevance feedback via Rocchio expansion for the TREC-2 and TREC-3 routing tasks.Error minimization is difficult in high-dimensional feature spaces because the convergence process is slow and the models~e prone to overfitting.We use two different strategies, latent semantic indexing and optimaJ term selection, to reduce the number of features.Our results indicate that features based on latent semantic indexing are more effective for techniques such as linear discriminant anafysis and logistic regression, which have no way to protect against overfitting.Neural networks perform equally well with either set of features and can take advantage of the additional information available when both feature sets are used as input.

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The majority of information retrieval experiments are evaluated by measures such as average precision and average recall. Fundamental decisions about the superiority of one retrieval technique over another are made solely on the basis of these measures. We claim that average performance figures need to be validated with a careful statistical analysis and that there is a great deal of additional information that can be uncovered by looking closely at the results of individual queries. This article is a case study of stemming algorithms which describes a number of novel approaches to evaluation and demonstrates their value.

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