@article {15324,
	title = {How Do Users Find Things with PubMed? Towards Automatic Utility Evaluation with User Simulations},
	year = {2008},
	month = {2008/02//},
	institution = {Human-Computer Interaction Lab, University of Maryland, College Park},
	abstract = {In the context of document retrieval in the biomedical domain, this paper explores the complex relationship between the quality of initial query results and the overall utility of an interactive system. We demonstrate that a content-similarity browsing tool can compensate for poor retrieval results, and that the relationship between retrieval performance and overall utility is non-linear. Arguments are advanced with user simulations, which characterize the relevance of documents that a user might encounter with different browsing strategies. With broader implications to IR, this work provides a case study of how user simulations can be exploited as a formative tool for automatic utility evaluation. Simulation-based studies provide researchers with an additional evaluation tool to complement interactive and Cranfield-style experiments.},
	keywords = {*BIOMEDICAL INFORMATION SYSTEMS, *BROWSING, *INFORMATION RETRIEVAL, *PUBMED, *SIMILARITY, *SIMULATION, *USER NEEDS, DOCUMENTS, FIND-SIMILAR, HUMAN FACTORS ENGINEERING \& MAN MACHINE SYSTEM, INFORMATION SCIENCE, MAN COMPUTER INTERFACE, QUALITY, queries, searching, TEST AND EVALUATION, UTILITY},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA478703},
	author = {Jimmy Lin and Smucker,Mark D.}
}
@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 {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}
}
