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Generating More-Positive and More-Negative Text.

, , and . Computing Attitude and Affect in Text, volume 20 of The Information Retrieval Series, Springer, (2006)

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Clustering the topics using TF-IDF for model fusion., and . PIKM, page 97-100. ACM, (2008)Generating More-Positive and More-Negative Text., , and . Computing Attitude and Affect in Text, volume 20 of The Information Retrieval Series, Springer, (2006)Semantic text similarity using corpus-based word similarity and string similarity., and . ACM Trans. Knowl. Discov. Data, 2 (2): 10:1-10:25 (2008)Building and Using a Lexical Knowledge Base of Near-Synonym Differences., and . Computational Linguistics, 32 (2): 223-262 (2006)Generating more-positive or more-negative text, , and . Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications, Stanford, California, (2004)A statistical model for near-synonym choice.. ACM Trans. Speech Lang. Process., 4 (1): 2:1-2:17 (2007)Automatic extraction of translations from web-based bilingual materials., , and . Mach. Transl., 21 (3): 139-163 (2007)Experiments for the Cross Language Speech Retrieval Task at CLEF 2006., and . CLEF, volume 4730 of Lecture Notes in Computer Science, page 778-785. Springer, (2006)Automatic Sense Disambiguation of the Near-Synonyms in a Dictionary Entry., and . CICLing, volume 2588 of Lecture Notes in Computer Science, page 258-267. Springer, (2003)Experiments on Extracting Knowledge from a Machine-Readable Dictionary of Synonym Differences (Invited Talk)., and . CICLing, volume 2004 of Lecture Notes in Computer Science, page 265-280. Springer, (2001)