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22. List of Contributors

Author

ALLEN, MARIE, primary, ALTING-MEES, MICHELLE A., additional, JOSÉ AYALA, FRANCISCO, additional, BÄHRING, SILVIA, additional, BANKIER, ALAN T., additional, BARRELL, BARCLAY G., additional, BAUER, STEVEN R., additional, BECK, STEPHAN, additional, BECKER-ANDRÉ, MICHAEL, additional, BEHR, JEAN-PAUL, additional, BHAGWAT, ASHOK S., additional, BHARUCHA, ADI D., additional, BIRNBOIM, H.C., additional, BOTTEMA, CYNTHIA D.K., additional, BOTTERMAN, JOHAN, additional, BRONSTEIN, IRENA, additional, BROWN, CAROL M., additional, BULL, MICHAEL, additional, CAI, ZELING, additional, CASSADY, JOSLYN D., additional, CATE, RICHARD L., additional, CHAI, KARL X., additional, CHAO, JULIE, additional, CHAO, LEE, additional, CHEE, MARK S., additional, CHEN, LIN, additional, COLECLOUGH, CHRISTOPHER, additional, CRAXTON, MOLLY, additional, DE BLOCK, MARC, additional, DE LA LUNA, SUSANA, additional, DE WET, JEFFREY R., additional, DENECKE, JÜRGEN, additional, D'HALLUIN, KATHLEEN, additional, DU, ZIJIN, additional, DUTTON, CHARYL M., additional, DWARKI, V.J., additional, EBERWINE, JAMES, additional, ECKELS, DAVID D., additional, EHRENFELS, CHRISTIAN W., additional, ERLICH, HENRY, additional, EVANS, GLEN A., additional, FERRO-LUZZI AMES, GIOVANNA, additional, FINNELL, RICHARD, additional, FROHMAN, MICHAEL A., additional, FULLER, CARL W., additional, GABRIELSEN, ODD S., additional, GARCIA, J. VICTOR, additional, GEE, MELISSA A., additional, JANE GEIGER, MARY, additional, GORSKI, JACK, additional, GUILFOYLE, TOM J., additional, GYLLENSTEN, ULF B., additional, HAGEN, GRETCHEN, additional, HANAFEY, MICHAEL K., additional, HARTL, DANIEL L., additional, HERMANSON, GARY G., additional, HO, STEFFAN N., additional, HOOD, LEROY, additional, HORTON, ROBERT M., additional, HRUBY, DENNIS E., additional, HUET, JANINE, additional, HUFFAKER, TIM C., additional, HUNT, HENRY D., additional, II, SETSUKO, additional, JANSSENS, JAN, additional, JONES, MICHAEL D., additional, JUNG, VINCENT, additional, KAWASAKI, ERNEST, additional, KREITMAN, MARTIN, additional, LANDWEBER, LAURA F., additional, LEEMANS, JAN, additional, LELONG, JEAN-CLAUDE, additional, LÉVESQUE, GEORGES, additional, LIEBER, ANDRE, additional, LOEFFLER, JEAN-PHILIPPE, additional, LUEHRSEN, KENNETH R., additional, LYNCH, CARMEL M., additional, MACFERRIN, KURTIS D., additional, MACKLER, SCOTT, additional, MAEDA, KAYO, additional, MALONE, ROBERT W., additional, MCCLURE, BRUCE A., additional, MILLER, A. DUSTY, additional, MILLER, DANIEL G., additional, MIYASHIRO, KEVIN, additional, MURPHY, OWEN J., additional, OCHMAN, HOWARD, additional, ORTÍU, JUAN, additional, PADDOCK, GARY V., additional, PADMANABHAN, R., additional, PEASE, LARRY R., additional, PESTKA, SIDNEY, additional, PESTKA, STEVEN B., additional, POTTER, HUNTINGTON, additional, POUSTKA, ANNEMARIE, additional, PULLEN, JEFFREY K., additional, RAFALSKI, J. ANTONI, additional, RAWLINSON, WILLIAM D., additional, REYNAERTS, ARLETTE, additional, RUSSELL, J.A., additional, SAIKI, RANDALL, additional, SANDIG, VOLKER, additional, SANFORD, J.C., additional, SARKAR, GOBINDA, additional, SCHEUERMANN, RICHARD H., additional, SCHREIBER, STUART L., additional, SCOTT, JAMIE K., additional, SCZAKIEL, GEORG, additional, SHORT, J.M., additional, SHYAMALA, VENKATAKRISHNA, additional, SINGH, HARINDER, additional, SMITH, F.D., additional, SMITH, GEORGE P., additional, SMITH, VICTORIA, additional, SNIDER, KEN, additional, SOH, JAEMOG, additional, SOMMER, WOLFGANG, additional, SOMMER, STEVE S., additional, SONG, YAH-RU, additional, SORGE, J.A., additional, SPENCER, CORINNE, additional, STRAUSS, MICHAEL, additional, TARTOF, KENNETH D., additional, TERRANOVA, MICHAEL P., additional, TINGEY, SCOTT V., additional, TIZARD, RICHARD, additional, VARNER, JOSEPH E., additional, VEN MURTHY, M.R., additional, VERDINE, GREGORY L., additional, VERMA, INDER M., additional, VOYTA, JOHN C., additional, WALBOT, VIRGINIA, additional, WHITTAKER, PAUL A., additional, WILLIAMS, JOHN G.K., additional, WILSON, ELIZABETH M., additional, WILSON, RICHARD K., additional, YE, ZHENG-HUA, additional, ZELENETZ, ANDREW D., additional, ZHANG, Q.X., additional, and ZHAO, L.-J., additional

Published
1995
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26. Identification of the Cis-Acting Elements and Trans-Acting Factors That Mediate Cell-Specific and Thyroid Hormone Stimulation of Growth Hormone Gene Expression

Author

SAMUELS, HERBERT H., primary, ARANDA, ANA, additional, CASANOVA, JUAN, additional, COPP, RICHARD P., additional, FLUG, FRANCES, additional, FORMAN, BARRY M., additional, HOROWITZ, ZEBULUN D., additional, JANOCKO, LAURA, additional, PARK, HAE-YOUNG, additional, PASCUAL, ANGEL, additional, RAAKA, BRUCE M., additional, SAHNOUN, HADJIRA, additional, STANLEY, FREDERICK, additional, YAFFE, BARRY M., additional, YANG, CHANG-REN, additional, and YE, ZHENG-SHENG, additional

Published
1988
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28. Filler-Gap Processing in Mandarin Relative Clauses: Evidence from Event-Related Potentials.

Author

Packard, Jerome L., Ye, Zheng, and Zhou, Xiaolin

Abstract

This chapter reports the results of an Event-Related Potential (ERP) study of relative clause processing in Mandarin Chinese. The objective of this research is to determine whether electrophysiological evidence of filler-gap integration costs found for other languages would be observed in Mandarin and to determine where in the relative clause filler-gap integration occurs. To pursue these goals, we analyze ERP data obtained from 20 Mandarin speakers as they read sentences containing subject-gap and object-gap relative clauses modifying the subjects and objects of matrix sentences. Our results indicate a larger P600 ERP component for subject-gap than object-gap relative clauses at the point in the clause where the filler is integrated with the antecedent gap. That effect was found on the relative clause marker de for clauses modifying the matrix subject and on the relative clause head for clauses modifying the matrix object. We interpret our results as supporting the hypothesis that the difficulty of integrating verb arguments is reflected in the magnitude of the P600 ERP component and that in Mandarin relative clauses, subject-gap arguments are more difficult to integrate than object-gap arguments. Based on these findings, we argue that in Mandarin, relative clause verb selectional restrictions may be satisfied in real time either by the relative clause head or by the relative clause marker de, depending on where the relative clause is located within the matrix sentence. [ABSTRACT FROM AUTHOR]

Published
2011
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29. Revisiting Rocchio΄s Relevance Feedback Algorithm for Probabilistic Models.

Author

Ye, Zheng, He, Ben, Huang, Xiangji, and Lin, Hongfei

Abstract

Rocchio΄s relevance feedback method enhances the retrieval performance of the classical vector space model. However, its application to the probabilistic models is not adequately explored. In this paper, we revisit Rocchio΄s algorithm by proposing to integrate this classical feedback method into the divergence from randomness (DFR) probabilistic framework for pseudo relevance feedback (PRF). Such an integration is denoted by RocDFR in this paper. In addition, we further improve RocDFR΄s robustness by proposing a quality-biased feedback method, called QRocDFR. Extensive experiments on standard TREC test collections show that our proposed RocDFR and QRocDFR methods significantly outperform the relevance model (RM3), which is a representative feedback model in the language modeling framework. Moreover, the QRocDFR method considerably improves the robustness of RocDFR΄s retrieval performance with respect to the size of feedback document set. [ABSTRACT FROM AUTHOR]

Published
2010
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30. Exploring Social Annotation Tags to Enhance Information Retrieval Performance.

Author

Ye, Zheng, Huang, Xiangji Jimmy, Jin, Song, and Lin, Hongfei

Abstract

Pseudo relevance feedback (PRF) via query expansion has proven to be effective in many information retrieval tasks. Most existing approaches are based on the assumption that the most informative terms in top-ranked documents from the first-pass retrieval can be viewed as the context of the query, and thus can be used to specify the information need. However, there may be irrelevant documents used in PRF (especially for hard topics), which can bring noise into the feedback process. The recent development of Web 2.0 technologies on Internet has provided an opportunity to enhance PRF as more and more high-quality resources can be freely obtained. In this paper, we propose a generative model to select high-quality feedback terms from social annotation tags. The main advantages of our proposed feedback model are as follows. First, our model explicitly explains how each feedback term is generated. Second, our model can take advantage of the human-annotated semantic relationship among terms. Experimental results on three TREC test datasets show that social annotation tags can be used as a good external resource for PRF. It is as good as the top-ranked documents from first-pass retrieval with optimal parameter setting on the WSJ dataset. When we combine the top-ranked documents and the social annotation tags, the retrieval performance can be further improved. [ABSTRACT FROM AUTHOR]

Published
2010
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31. Enhancing Content-Based Image Retrieval Using Machine Learning Techniques.

Author

Hu, Qinmin Vivian, Ye, Zheng, and Huang, Xiangji Jimmy

Abstract

In this paper, we propose a term selection model to help select terms in the documents that describe the images to improve the content-based image retrieval performance. First, we introduce a general feature selection model. Second, we present a painless way for training document collections, followed by selecting and ranking the terms using the Kullback-Leibler Divergence. After that, we learn the terms by the classification method, and test it on the content-based image retrieval result. Finally, we setup a series of experiments to confirm that the model is promising. Furthermore, we suggest the optimal values for the number maxK and the tuning combination parameter α in the experiments. [ABSTRACT FROM AUTHOR]

Published
2010
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32. Constructing Associative Classifier Using Rough Sets and Evidence Theory.

Author

Carbonell, Jaime G., Siekmann, Jörg, An, Aijun, Stefanowski, Jerzy, Ramanna, Sheela, Butz, Cory J., Pedrycz, Witold, Wang, Guoyin, Jiang, Yuan-Chun, Liu, Ye-Zheng, Liu, Xiao, and Zhang, Jie-Kui

Abstract

Constructing accurate classifier based on association rule is an important and challenging task in data mining. In this paper, a novel combination strategy based on rough sets (RST) and evidence theory (DST) for associative classification (RSETAC) is proposed. In RSETAC, rules are regarded as classification experts, after the calculation of the basic probability assignments (bpa) according to rule confidences and evidence weights employing RST, Yang's rule of combination is employed to combine the distinct evidences to realize an aggregate classification. A numerical example is shown to highlight the procedure of the proposed method. The comparison with popular methods like CBA, C4.5, RIPPER and MCAR indicates that RSETAC is a competitive method for classification based on association rule. [ABSTRACT FROM AUTHOR]

Published
2007
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33. Meaning, Life and Culture

Author

Bromhead, Helen and Ye, Zhengdao

Subjects
Anna Wierzbicka, Natural Semantic Metalanguage, linguistics, Semantics, language, Culture, Typology, bic Book Industry Communication::C Language::CF linguistics, bic Book Industry Communication::C Language::CF linguistics::CFG Semantics, discourse analysis, etc, bic Book Industry Communication::C Language::CF linguistics::CFK Grammar, syntax & morphology
Abstract

This book is dedicated to Anna Wierzbicka, one of the most influential and innovative linguists of her generation. Her work spans a number of disciplines, including anthropology, cultural psychology, cognitive science, philosophy and religious studies, as well as her home base of linguistics. She is best known for the Natural Semantic Metalanguage (NSM) approach to meaning—a versatile tool for exploring 'big questions' concerning the diversity and universals of people’s experience in the world. In this volume, Anna Wierzbicka’s former students, old and current colleagues, ‘kindred spirits’ and ‘sparring partners’ engage with her ideas and diverse body of work. These authors cover topics from the grammar of action verbs to cross-cultural pragmatics, and over 30 languages from around the world are represented. The chapters in Part 1 focus on the NSM approach and cover four themes: lexico-grammatical semantics, cultural keywords, semantics of nouns, and emotion. In Part 2, the contributors connect with a meaning-based approach from their own intellectual perspectives, including syntax, anthropology, cognitive linguistics and sociolinguistics. The deep humanistic perspective, wide-ranging themes and interdisciplinary nature of Wierzbicka’s research are reflected in the contributions. The common thread running through all chapters is the primacy of meaning to the understanding of language and culture.

Published
2020
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