Query Expansion : a comparative study of Automatic Query Expansion with and without relevance feedback

In query expansion (QE) terms are added to an initial query in order to improve retrieval effectiveness. In this thesis we use QE in the sense that a reformulation of the query is done by deleting the terms in the initial query and instead replacing them with terms from the documents retrieved in the initial run. The aim of this thesis is to, in a experimental full text invironment, study and compare the retrieval result of two different query expansion strategies in relation to each other. The following questions are addressed by the study: How do the two strategies perform in relation to each other regarding recall?What may be causing the result?Are the two strategies retrieving the same relevant documents?Two strategies are designed to simulate a searcher using automatic query expansion (AQE) either with or without relevance feedback. Strategy I is simulating AQE without relevance feedback by taking the top five documents that are retrieved in the initial run and then extracting the top ten most frequently occurring terms in these to create a new query. Correspondingly the Strategy II, is simulating AQE with relevance feedback by taking the top five relevant documents and extracting the top ten terms in these to create a new query. It is concluded that both of the strategies’ retrieval performance was improved for most of the topics. In average Strategy II did achieve 54.63 percent recall compared to Strategy I which did achieve 45.59 percent recall. The two strategies did retrieve different relevant documents for majority of the topics. Hence, it would be reasonable to base a system on both of them.Uppsatsnivå: