A Comparison of Two Corpus-Based Methods for Translingual Information Retrieval
In translingual information retrieval (TIR), ad hoc queries in any of a set of languages can be used to retrieve documents in any of a set of languages. Classical informationretrieval methods such as the vector-space model cannot be applied to TIR because they base similarity on the overlap of terms between queries and documents|this is typically zero in TIR. The generalized vector-space model (GVSM) and latent semantic indexing (LSI) are two variations of the vector-space model that make comparisons outside of term space. For this reason, both can be and have been applied to TIR. In this paper, we report on a series of experiments comparing the performance of GVSM and LSI on monolingual and translingual retrieval tasks. We nd that the performance of both methods depends crucially on parameter settings, that LSI performs better, and that GVSM runs more quickly.