@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 {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 {16718,
	title = {The Bible, Truth, and Multilingual OCR Evaluation},
	year = {1998},
	month = {1998/12//},
	institution = {University of Maryland, College Park},
	abstract = {Multilingual OCR has emerged as an important information technology, thanks to the increasing need for cross-language information access. While many research groups and companies have developed OCR algorithms for various languages, it is difficult to compare the performance of these OCR algorithms across languages. This difficulty arises because most evaluation methodologies rely on the use of a document image dataset in each of the languages and it is difficult to find document datasets in different languages that are similar in content and layout. In this paper we propose to use the Bible as a dataset for comparing OCR accuracy across languages. Besides being available in a wide range of languages, Bible translation are closely parallel in content, carefully translated, surprisingly relevant with respect to modern-day language, and quite inexpensive. A project at the University of Maryland is currently implementing this idea. We have created a scanned image dataset with groundtruth from an Arabic Bible. We have also used image degradation models to create synthetically degraded images of a French Bible. We hope to generate similar Bible datasets for other languages, and we are exploring alternative corpora such as the Koran and the Bhagavad Gita that have similar properties. Quantitative OCR evaluation based on the Arabic Bible dataset is currently in progress.},
	keywords = {*BIBLE, *CORPUS, *DATASETS, *GROUNDTRUTH, *OPTICAL CHARACTER RECOGNITION, *TEST SETS, *TRANSLATIONS, accuracy, algorithms, CYBERNETICS, DOCUMENT IMAGES, DOCUMENTS, IMAGES, INFORMATION SCIENCE, LANGUAGE, linguistics, MULTILINGUAL OCR(OPTICAL CHARACTER RECOGNITION), SYMPOSIA, TEST AND EVALUATION},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA458666},
	author = {Kanungo,Tapas and Resnik, Philip}
}
