@article {13784,
	title = {Headline Generation for Written and Broadcast News},
	year = {2005},
	month = {2005/03//},
	institution = {Instititue for Advanced Computer Studies, Univ of Maryland, College Park},
	abstract = {This technical report is an overview of work done on Headline Generation for written and broadcast news. The report covers HMM Hedge, a statistical approach based on the noisy channel model, Hedge Trimmer, a parse-and-trim approach using linguistically motivated trimming rules, and Topiary, a combination of Trimmer and Unsupervised Topic Discovery. Automatic evaluation of summaries using ROUGE and BLEU is described and used to evaluate the Headline Generation systems.},
	keywords = {*INFORMATION RETRIEVAL, *RADIO BROADCASTING, AUTOMATIC, AUTOMATIC SUMMARIZATION, HEDGE TRIMMER, INFORMATION SCIENCE, RADIO COMMUNICATIONS, STATISTICAL PROCESSES, TEST AND EVALUATION, WORDS(LANGUAGE)},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA454198},
	author = {Zajic, David and Dorr, Bonnie J and Schwartz,Richard}
}
@article {13759,
	title = {Domain-Specific Term-List Expansion Using Existing Linguistic Resources},
	year = {2002},
	month = {2002/08//},
	institution = {Instititue for Advanced Computer Studies, Univ of Maryland, College Park},
	abstract = {This report describes a series of experiments involving expansion of a domain-specific human-generated "seed list" using available linguistic resources. The resources used for the expansion are intended to be general purpose: two large-scale Chinese-English dictionaries and a Chinese lexical knowledge base (HowNet). The methodology involves three steps: (1) hand extraction of head words from each entry in the human-generated seed list; (2) automatic comparison of these head words against entries in the linguistic resources-where an entry matches if the head word matches the entry exactly or is included in its the semantic definition; and (3) collection of any resulting matching entries into a larger term list. The terms extracted by this process were verified manually to confirm whether they were relevant to the topic of a specific domain. An important contribution of this work is the finding that the use of a bilingual term list for the expansion proces does not provide a significant improvement over the use of a simpler, more easily produced, monoligual term list.},
	keywords = {*LEXICOGRAPHY, *LINGUISTICS, AUTOMATIC, CHINA, COMPARISON, EXTRACTION, HANDS, knowledge based systems, linguistics, resources, WORDS(LANGUAGE)},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA525800},
	author = {Dorr, Bonnie J and Zhao,Tiejun}
}
@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}
}
@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}
}
