Combining fields for query expansion and adaptive query expansion |
| |
| Authors: | Ben He Iadh Ounis |
| |
| Institution: | Department of Computing Science, University of Glasgow, Glasgow G12 8QQ, United Kingdom |
| |
| Abstract: | In this paper, we aim to improve query expansion for ad-hoc 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 WT10G 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. |
| |
| Keywords: | Query expansion on fields Pseudo relevance feedback Information retrieval External expansion Adaptive query expansion TREC experiments |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
