Combining fields for query expansion and adaptive query expansion

In this paper, we aim to improve query expansion for ad-hoe retrieval, by proposing a more fine-grained term reweighting process. This fine-grained process uses statistics from the representation of documents in various fields, such as their titles, the anchor text of their incoming links, and their body content. The contribution of this paper is twofold: First, we propose a novel query expansion mechanism on fields by combining field evidence available in a corpora. Second, we propose an adaptive query expansion mechanism that selects an appropriate collection resource, either the local collection, or a high-quality external resource, for query expansion on a per-query basis. The two proposed query expansion approaches are thoroughly evaluated using two standard Text Retrieval Conference (TREC) Web collections, namely the WTI OG collection and the large-scale GOV2 collection. From the experimental results, we observe a statistically significant improvement compared with the baselines. Moreover, we conclude that the adaptive query expansion mechanism is very effective when the external collection used is much larger than the local collection. (c) 2006 Elsevier Ltd. All rights reserved.