Cross-lingual Pseudo Relevance Feedback Based on Weak Relevant Topic Alignment

Abstract

In this paper, a cross-lingual pseudo relevance feedback (PRF) model based on weak relevant topic alignment (WRTA) is proposed for cross language query expansion on unparallel web pages. Topics in different languages are aligned on the basis of translation. Useful expansion terms are extracted from weak relevant topics according to the bilingual term similarity. Experiment results on web-derived unparalell data show the contribution of the WRTA-based PRF model to cross language information retrieval.

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Cite this paper

@inproceedings{Wang2015CrosslingualPR, title={Cross-lingual Pseudo Relevance Feedback Based on Weak Relevant Topic Alignment}, author={Xuwen Wang and Qiang Zhang and Xiaojie Wang and Junlian Li}, booktitle={PACLIC}, year={2015} }