@article {13827,
	title = {Mapping WorldNet Senses to a Lexical Database of Verbs},
	year = {2001},
	month = {2001/01//},
	institution = {Instititue for Advanced Computer Studies, Univ of Maryland, College Park},
	abstract = {This paper describes automatic techniques for mapping 9611 semantically classified English verbs to WordNet senses. The verbs were initially grouped into 491 semantic classes based on syntactic categories; they were then mapped into WordNet senses according to three pieces of information: (1) prior probability of WordNet senses; (2) semantic similarity of WordNet senses for verbs within the same category; and (3) probabilistic correlations between WordNet relationship and verb frame data. Our techniques make use of a training set of 1791 disambiguated entries representing 1442 verbs occurring in 167 of the categories. The best results achieved .58 recall and .72 precision, versus a lower bound of .38 recall and .62 precision for assigning the most frequently occurring WordNet sense, and an upper bound of .75 recall and .87 precision for human judgment.},
	keywords = {*DATA BASES, *ENGLISH LANGUAGE, *LEXICAL DATABASES, *LEXICOGRAPHY, *MAPPING, *VERBS, *WORD MAPPING, *WORDNET, AMBIGUITY, correlation, FRAMES, Frequency, INFORMATION SCIENCE, JUDGEMENT(PSYCHOLOGY), linguistics, PRECISION, probability, RECALL, semantics, syntax, WORDS(LANGUAGE), WSD(WORD SENSE DISAMBIGUATION)},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA458846},
	author = {Green,Rebecca and Pearl,Lisa and Dorr, Bonnie J}
}
@conference {18454,
	title = {Pictorial query trees for query specification in image databases},
	booktitle = {Fourteenth International Conference on Pattern Recognition, 1998. Proceedings},
	volume = {1},
	year = {1998},
	month = {1998/08//},
	pages = {919-921 vol.1 - 919-921 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A technique that enables specifying complex queries in image databases using pictorial query trees is presented. The leaves of a pictorial query tree correspond to individual pictorial queries that specify which objects should appear in the target images as well as how many occurrences of each object are required. In addition, the minimum required certainty of matching between query-image objects and database-image objects, as well as spatial constraints that specify bounds on the distance between objects and the relative direction between them are also specified. Internal nodes in the query tree represent logical operations (AND, OR, XOR) and their negations on the set of pictorial queries (or subtrees) represented by its children. The syntax of query trees is described. Algorithms for processing individual pictorial queries and for parsing and computing the overall result of a pictorial query tree are outlined},
	keywords = {Automation, complex queries, Computer science, content-based retrieval, Database systems, database-image objects, Educational institutions, Electrical capacitance tomography, grammars, Image databases, Image matching, parsing, pictorial query trees, Postal services, query specification, query-image objects, spatial constraints, syntax, visual databases},
	isbn = {0-8186-8512-3},
	doi = {10.1109/ICPR.1998.711383},
	author = {Soffer,A. and Samet, Hanan and Zotkin,Dmitry N}
}
@article {13704,
	title = {A Lexical Conceptual Approach to Generation for Machine Translation},
	year = {1988},
	month = {1988/01//},
	institution = {MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB},
	abstract = {Current approaches to generation for machine translation make use of direct-replacement templates, large grammars, and knowledge-based inferencing techniques. Not only are rules language-specific, but they are too simplistic to handle sentences that exhibit more complex phenomena. Furthermore, these systems are not easily extendable to other languages because the rules that map the internal representation to the surface form are entirely dependent on both the domain of the system and the language being generated. Finally an adequate interlingual representation has not yet been discovered; thus, knowledge-based inferencing is necessary and syntactic cross-linguistic generalization cannot be exploited. This report introduces a plan for the development of a theoretically based computational scheme of natural language generation for a translation system. The emphasis of the project is the mapping from the lexical conceptual structure of sentences to an underlying or base syntactic structure called deep structure. This approach tackles the problems of thematic and structural divergence, i.e., it allows generation of target language sentences that are not thematically or structurally equivalent to their conceptually equivalent source language counterparts. Two other more secondary tasks, construction of a dictionary and mapping from deep structure to surface structure, will also be discussed. The generator operates on a constrained grammatical theory rather than on a set of surface level tranformations.},
	keywords = {*MACHINE TRANSLATION, *NATURAL LANGUAGE, *SYSTEMS APPROACH, COMPUTATIONS, CYBERNETICS, Dictionaries, grammars, internal, LEXICOGRAPHY, SURFACES, syntax, THEORY, TRANSLATIONS, WORDS(LANGUAGE)},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA197356},
	author = {Dorr, Bonnie J}
}
