@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}
}
@article {13737,
	title = {Chinese-English Semantic Resource Construction},
	year = {2000},
	month = {2000/06//},
	institution = {Instititue for Advanced Computer Studies, Univ of Maryland, College Park},
	abstract = {We describe an approach to large-scale construction of a semantic lexicon for Chinese verbs. We leverage off of three existing resources-- a classification of English verbs called EVCA (English Verbs Classes and Alternations), a Chinese conceptual database called HowNet, and a large-machine readable dictionary called Optilex. The resulting lexicon is used for determining appropriate word senses in applications such as machine translation and cross-language information retrieval.},
	keywords = {*CHINESE LANGUAGE, *ENGLISH LANGUAGE, *LEXICONS, *SEMANTICS, *VERBS, *WORD SENSES, *WORDS(LANGUAGE), classification, CONCEPTS, CONSTRUCTION, Dictionaries, INFORMATION SCIENCE, linguistics, VERB CLASSES, Vocabulary},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA458703},
	author = {Dorr, Bonnie J and Levow,Gina-Anne and Lin,Dekang and Thomas,Scott}
}
@article {13819,
	title = {LEXICALL: Lexicon Construction for Foreign Language Tutoring},
	year = {1997},
	month = {1997///},
	institution = {Instititue for Advanced Computer Studies, Univ of Maryland, College Park},
	abstract = {We focus on the problem of building large repositories of lexical conceptual structure (LCS) representations for verbs in multiple languages. One of the main results of this work is the definition of a relation between broad semantic classes and LCS meaning components. Our acquisition program LEXICALL takes, as input, the result of previous work on verb classification and thematic grid tagging, and outputs LCS representations for different languages. These representations have been ported into English, Arabic and Spanish lexicons, each containing approximately 9000 verbs. We are currently using these lexicons in an operational foreign language tutoring and machine translation.},
	keywords = {*DATA ACQUISITION, *FOREIGN LANGUAGES, *MACHINE TRANSLATION, GRIDS, INFORMATION SCIENCE, LEXICOGRAPHY, LEXICONS, linguistics, Vocabulary},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA458641},
	author = {Dorr, Bonnie J}
}
@article {13878,
	title = {Toward Compact Monotonically Compositional Interlingua Using Lexical Aspect},
	year = {1997},
	month = {1997/12//},
	institution = {Instititue for Advanced Computer Studies, Univ of Maryland, College Park},
	abstract = {We describe a theoretical investigation into the semantic space described by our interlingua (IL), which currently has 191 main verb classes divided into 434 subclasses. represented by 237 distinct Lexical Conceptual Structures (LCSs). Using the model of aspect in Olsen (1994b, 1997a) monotonic aspectual composition we have identified 71 aspectually basic subclasses that are associated with one or more of 68 aspectually non-basic classes via some lexical ("type-shifting") rule (Bresnan 1982, Pinker 1984, Levin and Rappaport Hovav 1995). This allows us to refine the IL and address certain computational and theoretical issues at the same time. From a linguistic viewpoint, the expected benefits include a refinement of the aspectual model in (Olsen :1994b, 1997a) (which provides necessary but not sufficient conditions for aspectual composition), and a refinement of the verb classifications in (Levin 1993); we also expect our approach to eventually produce a systematic definition (in terms of LCSs and compositional operations) of the precise meaning components responsible for Levin{\textquoteright}s classification. Computationally, the lexicon is made more compact.},
	keywords = {*LEXICOGRAPHY, *SEMANTICS, COMPOSITION(PROPERTY), linguistics, MODELS, STRUCTURES, THEORY, Vocabulary},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA458248},
	author = {Dorr, Bonnie J and Olsen,Mari B and Thomas,Scott C}
}
