Cross-lingual latent semantic analysis

Abstract

Cross-lingual information retrieval is a difficult task typically involving query translation into multiple languages followed by monolingual retrieval in each language. Latent Semantic Analysis allows cross-lingual retrieval without translating queries by working from an already existing corpus of translations. Thus, collecting such a corpus obviates the need to construct complicated translation tools, making this technique particularly applicable to querying less commercially appealing languages. First, we extend work on retrieval from an English-French corpora split into training and test sets to examine the effects of training on a corpus from a completely different. Success is measured by the proportion of direct translations correctly considered most similar by Latent Semantic Analysis. Secondly, an English only similarity task from the literature is also extended to train on a different corpus to the one being tested on. Here the degradation in performance is measured through examining the variation in the correlations between the inter-document similarity judgements calculated by Latent Semantic Analysis and an experimentally derived baseline of human judgements of inter-document similarity. Higher order indexing schemes discarding uncommon terms, sparse matrix representations and the removal of factors with very low eigenvalues are used to enhance efficiency. Performance degradation from exogenous training is shown in both cases. The best results occur using stopping, log-entropy weighting and over 500 factors. References

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