In automatic query expansion, where queries are automatically expanded with terms not in the original query but extracted from initially retrieved top-ranked documents, each term in the top-ranked documents is evaluated for its usefulness as an expansion term to be added to the original query. As the evaluation measure, we have used since [1] a Term Selection Value function based on a combination of a relevance weight [3] derived from document frequencies and a sum of within-document term frequencies, so that terms that are speci c to top-ranked documents (relevance weight factor) and representative of each top-ranked document (term frequency factor) would be rated highly. While query expansion based on the TSV function above contributed to improvement in retrieval e ectiveness in the past experiments, recent experiments suggested selecting expansion terms based on both the relevance weight and the term frequencies might not always be the best strategy; speci cally, when the top-ranked documents, assumed to be relevant to the topic, turn out to be mostly non-relevant, the relevance weight seemed to lead to inappropriate selection of expansion terms. In this paper, we investigate how a relevance weight affects expansion term selection as the number of relevant documents in the top-ranked documents decreases and examine the e ectiveness of an alternative approach of not using a relevance weight in expansion term evaluation.