Interactive and Automatic Query Expansion: A Comparative Study with an Application on Arabic

Ghassan Kanaan,Basel Bani-Ismai,Sameh Ghwanmeh,Riyad Al-Shalabi

doi:10.3844/ajassp.2008.1433.1436

Abstract

Much attention has been paid to the relative effectiveness of Interactive Query Expansion (IQE) versus Automatic Query Expansion (AQE). This research has been shown that automatic query expansion (collection dependent) strategy gives better performance than no query expansion. The percentage of queries that are improved by AQE strategy is 57% with average precision equal to 43.2. Compared against AQE (collection dependent) strategy, IQE gives better average precision than AQE strategy. The percentage of queries that are improved by best IQE decision is 86% with average precision equal to 44.1. Evaluation process reveals that the value of n in AQE strategy that gave the optimal value of average precision for the whole query set is equal to one.

Highlights

Information Retrieval (IR) is a task of selecting documents from a database in response to a user's query and ranking them according to relevance
All or some of these expansion terms can be added to the query either by the user-interactive query expansion (IQE)-or by the retrieval system-Automatic Query Expansion (AQE)[3]
The wpq method of ranking terms for query expansion has been employed in this research; this has been shown to give good results for both AQE and IQE[2,7]

Summary

INTRODUCTION

Information Retrieval (IR) is a task of selecting documents from a database in response to a user's query and ranking them according to relevance. The main argument in favor of IQE is that interactive query expansion gives more control to the user As it is the user who decides the criteria for relevance in a search, the user should be able to make better decisions on which terms are likely to be useful[4]. The query dependent expansion strategy calculates which value of n is optimal for individual queries This may be implemented in an operational retrieval system by, for example, setting a threshold on the expansion term weights. Stemming of the remaining words with the objective of removing affixes (i.e., prefixes and suffixes) and allowing the retrieval of documents containing syntactic variations of query terms (e.g., connect, connecting, connected, etc.). Construction of term categorization structures such as thesaurus, or extraction of structure directly represented in the text

MATERIALS AND METHODS

AND DISCUSSION

CONCLUSION