@conference {16818,
	title = {Images in News},
	booktitle = {Pattern Recognition (ICPR), 2010 20th International Conference on},
	year = {2010},
	month = {2010/08//},
	pages = {3240 - 3243},
	abstract = {A system, called News Stand, is introduced that automatically extracts images from news articles. The system takes RSS feeds of news article and applies an online clustering algorithm so that articles belonging to the same news topic can be associated with the same cluster. Using the feature vector associated with the cluster, the images from news articles that form the cluster are extracted. First, the caption text associated with each of the images embedded in the news article is determined. This is done by analyzing the structure of the news article{\textquoteright}s HTML page. If the caption and feature vector of the cluster are found to contain keywords in common, then the image is added to an image repository. Additional meta-information are now associated with each image such as caption, cluster features, names of people in the news article, etc. A very large repository containing more than 983k images from 12 million news articles was built using this approach. This repository also contained more than 86.8 million keywords associated with the images. The key contribution of this work is that it combines clustering and natural language processing tasks to automatically create a large corpus of news images with good quality tags or meta-information so that interesting vision tasks can be performed on it.},
	keywords = {algorithm;feature, articles;news, clustering, clustering;publishing;, extraction;hypermedia, extraction;image, feeds;caption, HTML, images;news, LANGUAGE, languages;image, markup, page;RSS, processing;news, processing;pattern, repository;meta-information;natural, retrieval;natural, stand;online, text;feature, vector;image},
	doi = {10.1109/ICPR.2010.792},
	author = {Sankaranarayanan,J. and Samet, Hanan}
}
@article {17798,
	title = {Optimal models of disjunctive logic programs: semantics, complexity, and computation},
	journal = {Knowledge and Data Engineering, IEEE Transactions on},
	volume = {16},
	year = {2004},
	month = {2004/04//},
	pages = {487 - 503},
	abstract = {Almost all semantics for logic programs with negation identify a set, SEM(P), of models of program P, as the intended semantics of P, and any model M in this class is considered a possible meaning of P with regard to the semantics the user has in mind. Thus, for example, in the case of stable models [M. Gelfond et al., (1988)], choice models [D. Sacca et al., (1990)], answer sets [M. Gelfond et al., (1991)], etc., different possible models correspond to different ways of "completing" the incomplete information in the logic program. However, different end-users may have different ideas on which of these different models in SEM(P) is a reasonable one from their point of view. For instance, given SEM(P), user U<sub>1</sub> may prefer model M<sub>1</sub> isin;SEM(P) to model M<sub>2</sub> isin;SEM(P) based on some evaluation criterion that she has. We develop a logic program semantics based on optimal models. This semantics does not add yet another semantics to the logic programming arena - it takes as input an existing semantics SEM(P) and a user-specified objective function Obj, and yields a new semantics Opt(P)_ sube; SEM(P) that realizes the objective function within the framework of preferred models identified already by SEM(P). Thus, the user who may or may not know anything about logic programming has considerable flexibility in making the system reflect her own objectives by building "on top" of existing semantics known to the system. In addition to the declarative semantics, we provide a complete complexity analysis and algorithms to compute optimal models under varied conditions when SEM(P) is the stable model semantics, the minimal models semantics, and the all-models semantics.},
	keywords = {complexity;, computational, disjunctive, function;, knowledge, LANGUAGE, Logic, minimal, model, nonmonotonic, objective, optimisation;, OPTIMIZATION, problems;, program, Programming, programming;, reasoning;, representation;, semantics;, stable, user-specified},
	isbn = {1041-4347},
	doi = {10.1109/TKDE.2004.1269672},
	author = {Leone,N. and Scarcello,F. and V.S. Subrahmanian}
}
@article {13790,
	title = {Improved Word-Level Alignment: Injecting Knowledge about MT Divergences},
	year = {2002},
	month = {2002/02/14/},
	institution = {Instititue for Advanced Computer Studies, Univ of Maryland, College Park},
	abstract = {Word-level alignments of bilingual text (bitexts) are not an integral part of statistical machine translation models, but also useful for lexical acquisition, treebank construction. and part-of-speech tagging. The frequent occurrence of divergences, structural differences between languages, presents a great challenge to the alignment task. We resolve some of the most prevalent divergence cases by using syntactic parse information to transform the sentence structure of one language to bear a closer resemblance to that of the other language. In this paper, we show that common divergence types can be found in multiple language pairs (in particular, we focus on English-Spanish and English-Arabic) and systematically identified. We describe our techniques for modifying English parse trees to form resulting sentences that share more similarity with the sentences in the other languages; finally, we present an empirical analysis comparing the complexities of performing word-level alignments with an without divergence handling. Our results suggest that divergence-handling can improve word-level alignment.},
	keywords = {*LEXICOGRAPHY, *MACHINE TRANSLATION, *STATISTICAL ANALYSIS, *WORDS(LANGUAGE), ACQUISITION, ALIGNMENT, EXPERIMENTAL DATA, LANGUAGE, linguistics, MATHEMATICAL MODELS, STATISTICS AND PROBABILITY, TREES},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA458774},
	author = {Dorr, Bonnie J and Pearl,Lisa and Hwa,Rebecca and Habash,Nizar}
}
@conference {18156,
	title = {A recognition algorithm for Chinese characters in diverse fonts},
	booktitle = {Image Processing. 2002. Proceedings. 2002 International Conference on},
	volume = {3},
	year = {2002},
	month = {2002/06//},
	pages = {981 - 984 vol.3 - 981 - 984 vol.3},
	abstract = {The paper proposes an algorithm for recognizing Chinese characters in many diverse fonts including Song, Fang, Kai, Hei, Yuan, Lishu, Weibei and Xingkai. The algorithm is based on features derived from peripheral direction contributions and utilizes a set of dictionaries. A 3-level matching is first performed with respect to each dictionary. The distance measures associated with these matches are then fed into a central discriminator to output the final recognition result. We propose a new multi-dictionary matching algorithm for use in the central discriminator that utilizes estimated information of neighborhood fonts. Experiments have been performed on a practical OCR software system whose recognition kernel is based on the proposed algorithm. Fast and accurate recognition has been accomplished both in title recognition, involving all of the 8 fonts, and in main-body recognition, that usually involves only the first 4 most commonly used fonts.},
	keywords = {algorithm;, central, character, Chinese, contributions;, direction, discriminator;, diverse, fonts;, image, interfaces;, LANGUAGE, MATCHING, matching;, multi-dictionary, natural, OCR, optical, peripheral, recognition, recognition;, sets;, software, system;},
	doi = {10.1109/ICIP.2002.1039139},
	author = {Wu,Xianli and M. Wu}
}
@article {16720,
	title = {Word-level Alignment for Multilingual Resource Acquisition},
	year = {2002},
	month = {2002/04//},
	institution = {Instititue for Advanced Computer Studies, Univ of Maryland, College Park},
	abstract = {We present a simple, one-pass word alignment algorithm for parallel text. Our algorithm utilizes synchronous parsing and takes advantage of existing syntactic annotations. In our experiments the performance of this model is comparable to more complicated iterative methods. We discuss the challenges and potential benefits of using the model to train syntactic parsers for new languages.},
	keywords = {*LEARNING MACHINES, *MULTILINGUAL RESOURCES, *WORDS(LANGUAGE), ACQUISITION, algorithms, ALIGNMENT, BENEFITS, ITERATIONS, LANGUAGE, linguistics},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA458782},
	author = {Lopez,Adam and Nossal,Michael and Hwa,Rebecca and Resnik, Philip}
}
@article {13761,
	title = {Efficient Language Independent Generation from Lexical Conceptual Structure},
	year = {2001},
	month = {2001/09//},
	institution = {Instititue for Advanced Computer Studies, Univ of Maryland, College Park},
	abstract = {This paper describes a system for generating natural-language sentences from an interlingual representation, Lexical Conceptual Structure (LCS). The system has been developed as part of a Chinese-English Machine Translation system; however, it is designed to be used for many other MT language pairs and Natural Language applications. The contributions of this work include: (1) Development of a language-independent generation system that maximizes efficiency through the use of a hybrid rule-based/statistical module; (2) Enhancements to an interlingual representation and associated algorithms for interpretation of multiply ambiguous input sentences; (3) Development of an efficient reusable language-independent linearization module with a grammar description language that can be used with other systems; (4) Improvements to an earlier algorithm for hierarchically mapping thematic roles to surface positions; (5) Development of a diagnostic tool for lexicon coverage and correctness and use of the tool for verification of English, Spanish, and Chinese lexicons. An evaluation of translation quality shows comparable performance with commercial translation system. The generation system can also be straightforwardly extended to their languages and this is demonstrated and evaluated for Spanish.},
	keywords = {*LEXICAL CONCEPTUAL STRUCTURE, *LEXICOGRAPHY, *MACHINE TRANSLATION, *VOCABULARY, *WORDS(LANGUAGE), algorithms, CHINA, COMMERCE, DIAGNOSTIC EQUIPMENT, efficiency, English language, grammars, INPUT, INTERLINGUAL, LANGUAGE, LANGUAGE-INDEPENDENT NLP., linguistics, MAPPING, natural language, QUALITY, SPANISH LANGUAGE, verification},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA458756},
	author = {Habash,Nizar and Dorr, Bonnie J and Traum,David}
}
@article {13809,
	title = {Large-Scale Construction of a Chinese-English Semantic Hierarchy},
	year = {2000},
	month = {2000/04//},
	institution = {Instititue for Advanced Computer Studies, Univ of Maryland, College Park},
	abstract = {This paper addresses the problem of building conceptual resources for multilingual applications. We describe new techniques for large-scale construction of a semantic hierarchy for Chinese verbs, using thematic-role information to create links between Chinese concepts and English classes. We then present an approach to compensating for gaps in the existing resources. The resulting hierarchy is used for a multilingual lexicon for Chinese-English machine translation and cross-language information retrieval applications.},
	keywords = {*CHINESE VERBS, *HIERARCHIES, *SEMANTICS, CHINA, CROSS CORRELATION, Information retrieval, INFORMATION SCIENCE, LANGUAGE, LEXICONS, linguistics, Machine translation, MULTILINGUAL, operations research, SYSTEMS APPROACH},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA458682},
	author = {Dorr, Bonnie J and Levow,Gina-Anne and Lin,Dekang}
}
@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}
}
@article {13842,
	title = {Principle-Based Parsing for Machine Translation},
	year = {1987},
	month = {1987/12//},
	institution = {MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB},
	abstract = {Many syntactic parsing strategies for machine translation systems are based entirely on context-free grammars. These parsers require an overwhelming number of rules; thus, translation systems using rule-based parsers either have limited linguistic coverage, or they have poor performance due to formidable grammar size. This report shows how a principle-based parser with a co-routine design improves parsing for translation. The parser consists of a skeletal structure-building mechanism that operates in conjunction with a linguistically based constraint module, passing control back and forth until a set of underspecified skeletal phrase-structures is converted into a fully instantiated parse tree. The modularity of the parsing design accommodates linguistic generalization, reduces the grammar size, allows extension to other languages, and is compatible with studies of human language processing. Keywords: Natural language processing, Interlingual translation, Parsing, Subroutines, Principles vs. Rules, Co-routine design, Linguistic constraints.},
	keywords = {*MACHINE TRANSLATION, *PARSERS, *SYNTAX, CONTROL, COROUTINE DESIGN, CYBERNETICS, grammars, HUMANS, LANGUAGE, linguistics, MODULAR CONSTRUCTION, natural language, PRINCIPLES BASED PARSERS, PROCESSING, STRATEGY, SUBROUTINES},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA199183},
	author = {Dorr, Bonnie J}
}
