Comparison of Global Term Expansion Methods for Text Retrieval

Abstract This paper describes our work at the fifth NTCIR workshop on the subtasks of single language information retrieval (SLIR). Several automatic global query expansion strategies were explored based on a machine-derive thesaurus. These term selection strategies were compared with manual selection and local expansion. Experiments show that all the global expansion strategies perform worse than the simple local expansion. Furthermore, even with the help of a human in selecting the global terms, the performance may not be better than an automatic local feedback method, if the human does not fully understand the information need of the search topic. However, the machine-derived thesaurus does extract relevant global terms for expansion. Proper term selection can yield great improvement in performance than local feedback alone. Keywords: Chinese IR, term association, global expansion. 1. Introduction In NTCIR-3, we participated in the Chinese, Japanese, and Korean single-language retrieval tasks (SLIRs) using an information retrieval system that dealt with these three languages in exactly the same way without using language-dependent knowledge or resources [1]. Results showed that our retrieval effectiveness does not show any difference among these three SLIRs. However, the effectiveness of our system was lower than the average of all runs submitted to the NTCIR by all participants. Post-analysis showed that the basic retrieval strategies used in our and others’ systems did not match top-performing systems that used other sophisticated techniques such as blind relevance feedback (BRF), probabilistic retrieval model, hybrid term indexing, and title words re-weighting [2-4]. Among these sophisticated techniques, BRF is the major approach that improves performance most. Relevance feedback is a technique that modifies the original query based on the initial retrieval results. If relevant (or irrelevant) terms can be identified from the initial results, adding them to (or subtracting them from) the original query for another run of retrieval often improves the retrieval effectiveness. However, since relevant terms are unknown to the system until the initial results were inspected and feedbacked by a searcher, most BRF methods under automatic retrieval mode simply assume that the top-ranked documents retrieved from the initial query are relevant. Terms are then extracted from these “relevant” documents so as to add to the initial query. This way of query modification or query expansion is called “local expansion” since only a handful of documents “relevant” to the initial query are used. Information from the rest of the documents is not exploited at all. In contrast, if the feedback information comes from the entire collection, we call this way of relevance feedback “global expansion”. Our preliminary experiments in NTCIR-4 showed that if best term selection can be achieved, query expansion based on the feedback terms from the entire collection can perform similarly well with local BRF and combining both local and global expansion can outperform each method alone [5]. A number of global expansion methods has been proposed in the past literature. A common way is to use a thesaurus, which is a powerful search aide tool heavily referred to in information access services. A thesaurus lists candidate search terms and relationships among them. This vocabulary knowledge can be used for broadening or narrowing the user’s search topics, or for suggesting synonyms or related terms to reduce the vocabulary mismatch problem, which has long been one of the major