@inbook {13825,
	title = {Machine Translation: Interlingual Methods},
	booktitle = {Encyclopedia of Language \& Linguistics (Second Edition)Encyclopedia of Language \& Linguistics (Second Edition)},
	year = {2006},
	month = {2006///},
	pages = {383 - 394},
	publisher = {Elsevier},
	organization = {Elsevier},
	address = {Oxford},
	abstract = {An interlingua is a notation for representing the content of a text that abstracts away from the characteristics of the language itself and focuses on the meaning (semantics) alone. Interlinguas are typically used as pivot representations in machine translation, allowing the contents of a source text to be generated in many different target languages. Because of the complexities involved, few interlinguas are more than demonstration prototypes, and only one has been used in a commercial MT system. In this article, we define the components of an interlingua and the principal issues faced by designers and builders of interlinguas and interlingua MT systems, illustrating with examples from operational systems and research prototypes. We discuss current efforts to annotate texts with interlingua-based information.},
	keywords = {approximate interlingua, compositionality, conceptual knowledge, cross-language divergences, interlingua, interlingual speech translation, language-independent representation, lexical-conceptual structure, Machine translation, ontology, predicate-argument structure, semantic annotation, semantic frames, semantic zones, text-meaning representations, THEMATIC ROLES},
	isbn = {978-0-08-044854-1},
	url = {http://www.sciencedirect.com/science/article/pii/B0080448542009391},
	author = {Dorr, Bonnie J and Hovy,E. and Levin,L.},
	editor = {Editor-in-Chief:~~ Keith Brown}
}
@article {13747,
	title = {Construction of a Chinese-English Verb Lexicon for Embedded Machine Translation in Cross-Language Information Retrieval},
	year = {2002},
	month = {2002/08//},
	institution = {Instititue for Advanced Computer Studies, Univ of Maryland, College Park},
	abstract = {This paper addresses the problem of automatic acquisition of lexical knowledge for rapid construction of MT engines for use in multilingual applications. We describe new techniques for large-scale construction of a Chinese-English verb lexicon and we evaluate the coverage and effectiveness of the resulting lexicon for a structured MT approach that is embedded in a cross-language information retrieval system. Leveraging off an existing Chinese conceptual database called HowNet and a large, semantically rich English verb database, we use thematic-role information to create links between Chinese concepts and English classes. We apply the metrics of recall and precision to evaluate the coverage and effectiveness of the linguistic resources. The results of this work indicate that: (1) we are able to obtain reliable Chinese-English entries both with and without pre-existing semantic links between the two languages; (2) if we have pre-existing semantic links, we are able to produce a more robust lexical resource by merging these with our semantically rich English database; (3) In our comparisons with manual lexicon creation, our automatic techniques were shown to achieve 62\% precision, compared to a much lower precision of 10\% for arbitrary assignment of semantic links.},
	keywords = {*ACQUISITION, *AUTOMATIC, *CROSS-LANGUAGE INFORMATION RETRIEVAL, *INFORMATION RETRIEVAL, *LEXICAL ACQUISITION, *MACHINE TRANSLATION, *VOCABULARY, Chinese language, CHINESE-ENGLISH LEXICONS, DATA BASES, EMBEDDED MT(MACHINE TRANSLATION) RESOURCES, EMBEDDING, English language, INFORMATION SCIENCE, linguistics, semantics, THEMATIC ROLES},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA459245},
	author = {Dorr, Bonnie J and Lin,Dekang and Levow,Gina-Anne}
}
