Abstract
Previous attempts at identifying translational equivalents in comparable corpora have dealt with very large 'general language' corpora and words. We address this task in a specialized domain, medicine, starting from smaller non-parallel, comparable corpora and an initial bilingual medical lexicon. We compare the distributional contexts of source and target words, testing several weighting factors and similarity measures. On a test set of frequently occurring words, for the best combination (the Jaccard similarity measure with or without tf.idf weighting), the correct translation is ranked first for 20% of our test words, and is found in the top 10 candidates for 50% of them. An additional reverse-translation filtering step improves the precision of the top candidate translation up to 74%, with a 33% recall.
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