Query expansion is the process of supplementing an original query with additional terms in order to refine a search and increase retrieval effectiveness. If the query expansion is interactive, then the user and the system work together to expand the query. The system usually suggests possible expansion terms and the user selects those they wish to add to the query. Studies have shown that interactive query expansion has the potential to improve retrieval effectiveness, but that it rarely succeeds in achieving its potential. It has been shown that users desire some control over the expansion process. In order to achieve this, the functionality of the system must be represented on the user interface in a comprehensible way. The main aim of this study is to focus on a small aspect of the interface and investigate whether the method used to present potential query expansion terms has any effect on retrieval effectiveness. The tool tested in this study automatically generates a hierarchical...

We describe an approach to information retrieval using Latent Semantic Indexing (LSI) that directly manipulates the values in the Singular Value Decomposition (SVD) matrices. We convert the dense term by dimension matrix into a sparse matrix by removing a fixed percentage of the values. We present retrieval and runtime performance results, using seven collections, which show that using this technique to remove up 70 % of the values in the term by dimension matrix results in similar or improved retrieval performance (as compared to LSI), while reducing memory requirements and query response time. Removal of 90 % of the values results in significantly reduced memory requirements and dramatic improvements in query response time. Removal of 90 % of the values degrades retrieval performance slightly for smaller collections, but improves retrieval performance by 60 % on the large collection we tested. 1

This work investigates the performance of five pattern classification algorithms in predicting natural contributions flow in a hydropower generation network, using a database of historical hydrological data. We compared the use of a multilayer perceptron, trained with the Resilient Backpropagation (RPROP) algorithm, vs. four well-known machine-learning techniques, as part of a software framework adapted to hydropower system assessment. The framework uses variable prediction algorithms to support rule-based decision processes. Our results are that the use of a neural network far outweighs

In this article, we report our implementation and comparison of two text clustering techniques. One is based on Ward's clustering and the other on Kohonen's Self-organizing Maps. We have evaluated how closely clusters produced by a computer resemble those created by human experts. We have also measured the time that it takes for an expert to "clean up" the automatically produced clusters. The technique based on Ward's clustering was found to be more precise. Both techniques have worked equally well in detecting associations between text documents. We used text messages obtained from group brainstorming meetings. q 1999 Elsevier Science B.V. All rights reserved.

Abstract not found

Abstract not found