@article {13758,
	title = {Domain Tuning of Bilingual Lexicons for MT},
	year = {2003},
	month = {2003/02//},
	institution = {Instititue for Advanced Computer Studies, Univ of Maryland, College Park},
	abstract = {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 IR Ni-style NIT system. Our domain-tuned lexicons brought about an improvement in the Blen 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.},
	keywords = {*DICTIONARIES, *FOREIGN LANGUAGES, accuracy, BILINGUAL LEXICONS, DOCUMENTS, linguistics, Vocabulary},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA455197},
	author = {Ayan,Necip F and Dorr, Bonnie J and Kolak,Okan}
}
