Language model information retrieval depends on accurate estimation of document models. In this paper, we propose a document expansion technique to deal with the problem of insufficient sampling of documents. We construct a probabilistic neighborhood for each document, and expand the document with its neighborhood information. The expanded document provides a more accurate estimation of the document model, thus improves retrieval accuracy. Moreover, since document expansion and pseudo feedback exploit different corpus structures, they can be combined to further improve performance. The experiment results on several different data sets demonstrate the effectiveness of the proposed document expansion method.
Tao et al. REF proposed a document expansion approach to enrich document representation before estimating document models.
Language Model Information Retrieval With Document Expansion
Human Language Technology Conference And Meeting Of The North American Association For Computational Linguistics
Computer Science