We dub our model ONETA (OrthoNormal Explicit Topic Analysis) and empirically show that on a cross-lingual retrieval task it outperforms ESA, LSI, and Latent Dirichlet Allocation (LDA) as well as a baseline consisting of translating each word into the target language, thus reducing the task to a standard monolingual matching task.
we quantify the effect of different approximation techniques for computing the orthonormal basis and investigate the effect of various methods for the normalization of frequency vectors.