Our overall objective is to translate a domain-specific document in a foreign language (in this case, Chinese) to English. Using automatically induced domain-specific, comparable documents and language-independent clustering, we apply domain-tuning techniques to a bilingual lexicon for downstream translation of the input document to English. We will describe our domain-tuning technique and demonstrate its effectiveness by comparing our results to manually constructed domain-specific vocabulary. Our coverage/accuracy experiments indicate that domain-tuned lexicons achieve 88% precision and 66% recall. We also ran a Bleu experiment to compare our domain-tuned version to its un-tuned counterpart in an IBM-style MT system. Our domain-tuned lexicons brought about an improvement in the Bleu scores: 9.4% higher than a system trained on a uniformly-weighted
dictionary and 275% higher than a system trained on no dictionary at all. UMIACS-TR-2003-19 LAMP-TR-096