Thu, 21 Aug 2008 15:12:21 BST CiteULike: hehrig Wiebe http://www.citeulike.org/user/hehrig/author/Wiebe CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/hehrig/article/2937481 <i>(June 2008), pp. 129-137.</i> Discourse Level Opinion Relations: An Annotation Study Swapna Somasundaran Josef Ruppenhofer Janyce Wiebe (June 2008), pp. 129-137. 2008-06-27T14:41:33-00:00 2008 129 137 Association for Computational Linguistics annotation http://www.citeulike.org/user/hehrig/article/643088 <i>Computers and the Humanities, Vol. 39, No. 2-3. (May 2005), pp. 165-210.</i> Annotating Expressions of Opinions and Emotions in Language Wiebe Janyce Wilson Theresa Cardie Claire doi:10.1007/s10579-005-7880-9 Computers and the Humanities, Vol. 39, No. 2-3. (May 2005), pp. 165-210. 2006-05-18T03:32:37-00:00 2005 Computers and the Humanities 1574-020X 39 2-3 165 210 Springer annotation http://www.citeulike.org/user/hehrig/article/2732881 <i>(1994), pp. 139-145.</i><br /><br />Most probabilistic classifiers used for word-sense disambiguation have either been based on only one contextual feature or have used a model that is simply assumed to characterize the interdependencies among multiple contextual features. In this paper, a different approach to formulating a probabilistic model is presented along with a case study of the performance of models produced in this manner for the disambiguation of the noun interest. We describe a method for formulating ... Word sense disambiguation using decomposable models Rebecca Bruce Janyce Wiebe (1994), pp. 139-145. 2008-04-29T09:07:14-00:00 1994 139 145 disambiguation http://www.citeulike.org/user/hehrig/article/2516371 <i>(2003)</i><br /><br />This paper presents a bootstrapping process that learns linguistically rich extraction patterns for subjective (opinionated) expressions. Learning Extraction Patterns for Subjective Expressions E Riloff J Wiebe (2003) 2008-03-11T19:34:24-00:00 2003 subjectivity-tagging http://www.citeulike.org/user/hehrig/article/1444203 <i>(2004), pp. 761-769.</i><br /><br />There has been a recent swell of interest in the automatic identification and extraction of opinions and emotions in text. In this paper, we present the first experimental results classifying the strength of opinions and other types of subjectivity and classifying the subjectivity of deeply nested clauses. We use a wide range of features, including new syntactic features developed for opinion recognition. In 10-fold crossvalidation experiments using support vector regression, we achieve improvements in mean-squared error over baseline ranging from 57\% to 64\%. Just how mad are you? Finding strong and weak opinion clauses Theresa Wilson Janyce Wiebe Rebecca Hwa (2004), pp. 761-769. 2007-07-09T12:28:45-00:00 2004 761 769 AAAI Press / The MIT Press sentiment-orientation http://www.citeulike.org/user/hehrig/article/1444195 <i>Vol. 3406 (2005), pp. 486-497.</i><br /><br />This paper presents the results of developing subjectivity classifiers using only unannotated texts for training. The performance rivals that of previous supervised learning approaches. In addition, we advance the state of the art in objective sentence classification, by learning extraction patterns associated with objectivity and creating objective classifiers that achieve substantially higher recall than previous work with comparable precision. Creating Subjective and Objective Sentence Classifiers from Unannotated Texts Janyce Wiebe Ellen Riloff Vol. 3406 (2005), pp. 486-497. 2007-07-09T12:28:45-00:00 2005 3406 486 497 Springer-Verlag sentiment-classification http://www.citeulike.org/user/hehrig/article/2332882 <i>(2000), pp. 299-305.</i> Effects of adjective orientation and gradability on sentence subjectivity Vasileios Hatzivassiloglou Janyce Wiebe doi:10.3115/990820.990864 (2000), pp. 299-305. 2008-02-05T01:45:13-00:00 2000 299 305 Association for Computational Linguistics opinion-mining sentiment http://www.citeulike.org/user/hehrig/article/2334590 <i>(2000), pp. 735-740.</i><br /><br />Subjectivity tagging is distinguishing sentences used to present opinions and evaluations from sentences used to objectively present factual information. There are numerous applications for which subjectivity tagging is relevant, including information extraction and information retrieval. This paper identifies strong clues of subjectivity using the results of a method for clustering words according to distributional similarity (Lin 1998), seeded by a small amount of detailed manual annotation.... Learning Subjective Adjectives from Corpora Janyce Wiebe (2000), pp. 735-740. 2008-02-05T12:11:17-00:00 2000 735 740 bibtex-import opinion-mining