Smoothing Methods and Cross-Language Document Re-ranking

Abstract

This paper presents a report on our participation in the CLEF 2009 monolingual and bilingual ad hoc TEL@CLEF task involving three different languages: English, French and German. Language modeling was adopted as the underlying information retrieval model. While the data collection is extremely sparse, smoothing is particularly important when estimating a language model. The main purpose of the monolingual tasks is to compare different smoothing strategies and investigate the effectiveness of each alternative. This retrieval model was then used alongside a document re-ranking method based on Latent Dirichlet Allocation (LDA) which exploits the implicit structure of the documents with respect to original queries for the monolingual and bilingual tasks. Experimental results demonstrated that three smoothing strategies behave differently across testing languages while the LDA-based document re-ranking method should be considered further in order to bring significant improvement over the baseline language modeling systems in the cross-language setting.