Query Log Analysis for Adaptive Dialogue-Driven Search

Abstract

The theme of this chapter is the improvement of Information Retrieval and Question Answering systems by the analysis of query logs. Two case studies are discussed. The first describes an intranet search engine working on a university campus which can present sophisticated query modifications to the user. It does this via a hierarchical domain model built using multi-word term co-occurrence data. The usage log was analysed using mutual information scores between a query and its refinement, between a query and its replacement, and between two queries occurring in the same session. The results can be used to validate refinements in the domain model, and to suggest replacements such as domain-dependent spelling corrections. The second case study describes a dialogue-based question answering system working over a closed document collection largely derived from the Web. Logs here are based around explicit sessions in which an analyst interacts with the system. Analysis of the logs has shown that certain types of interaction lead to increased precision of the results. Future versions of the system will encourage these forms of interaction. The conclusions of this chapter are firstly that there is a growing literature on query log analysis, much of it reviewed here, secondly that logs provide many forms of useful information for improving a system, and thirdly that mutual information measures taken with automatic term recognition algorithms and hierarchy construction techniques comprise one approach for enhancing system performance.