Abstract

The relevance feedback process uses information obtained from a user about a set of initially retrieved documents to improve subsequent search formulations and retrieval performance. In extended Boolean models, the relevance feedback implies not only that new query terms must be identified and re-weighted, but also that the terms must be connected with Boolean And/Or operators properly. Salton et al. proposed a relevance feedback method, called DNF (disjunctive normal form) method, for a well established extended Boolean model. However, this method mainly focuses on generating Boolean queries but does not concern about re-weighting query terms. Also, this method has some problems in generating reformulated Boolean queries. In this study, we investigate the problems of the DNF method and propose a relevance feedback method using hierarchical clustering techniques to solve those problems. We also propose a neural network model in which the term weights used in extended Boolean queries can be adjusted by the users’ relevance feedbacks.

Keywords: Relevance feedback; Extended Boolean model; Hierarchical clustering; Multi-layer perceptron

Article Outline

1. Introduction

2. Related works

2.1. P-Norm based extended Boolean model

2.2. Relevance feedback methods in extended Boolean model

3. Hierarchical clustering relevance feedback method

3.1. Generating cluster tree

3.2. Generating extended Boolean query in DNF

4. Neural network model for re-weighting terms

4.1. Neural network structure for extended Boolean expressions

4.2. Activation function and learning rule

5. Experiments and results

5.1. Experiment 1

5.2. Experiment 2

6. Conclusions

Acknowledgements

References