acl acl2010 acl2010-42 acl2010-42-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Ainur Yessenalina ; Yejin Choi ; Claire Cardie
Abstract: One ofthe central challenges in sentimentbased text categorization is that not every portion of a document is equally informative for inferring the overall sentiment of the document. Previous research has shown that enriching the sentiment labels with human annotators’ “rationales” can produce substantial improvements in categorization performance (Zaidan et al., 2007). We explore methods to automatically generate annotator rationales for document-level sentiment classification. Rather unexpectedly, we find the automatically generated rationales just as helpful as human rationales.
John Blitzer, Mark Dredze, and Fernando Pereira. 2007. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 440–447, Prague, Czech Republic, June. Association for Computational Linguistics. Thorsten Joachims. 1999. Making large-scale support vector machine learning practical. pages 169–184. Yi Mao and Guy Lebanon. 2006. Sequential models for sentiment prediction. In Proceedings of the ICML Workshop: Learning in Structured Output Spaces Open Problems in Statistical Relational Learning Statistical Network Analy- sis: Models, Issues and New Directions. Ryan McDonald, Kerry Hannan, Tyler Neylon, Mike Wells, and Jeff Reynar. 2007. Structured models for fine-tocoarse sentiment analysis. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 432–439, Prague, Czech Republic, June. Association for Computational Linguistics. Bo Pang and Lillian Lee. 2004. A sentimental education: sentiment analysis using subjectivity summarizationbased on minimum cuts. In ACL ’04: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, page 271, Morristown, NJ, USA. Association for Computational Linguistics. Bo Pang and Lillian Lee. 2008. Opinion mining and sentiment analysis. Found. Trends Inf. Retr., 2(1-2): 1–135. Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002. Thumbs up?: sentiment classification using machine learning techniques. In EMNLP ’02: Proceedings of the ACL-02 conference on Empirical methods in natural language processing, pages 79–86, Morristown, NJ, USA. Association for Computational Linguistics. Robert E. Schapire, Marie Rochery, Mazin G. Rahim, and Narendra Gupta. 2002. Incorporating prior knowledge into boosting. In ICML ’02: Proceedings of the Nineteenth International Conference on Machine Learning, pages 538–545, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc. Maite Taboada, Julian Brooke, and Manfred Stede. 2009. Genre-based paragraph classification for sentiment analysis. In Proceedings of the SIGDIAL 2009 Conference, pages 62–70, London, UK, September. Association for Computational Linguistics. Janyce Wiebe, Theresa Wilson, and Claire Cardie. 2005. Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, 1(2):0. Theresa Wilson, Paul Hoffmann, Swapna Somasundaran, Jason Kessler, Janyce Wiebe, Yejin Choi, Claire Cardie, Ellen Riloff, and Siddharth Patwardhan. 2005a. Opinionfinder: a system for subjectivity analysis. In Proceedings of HLT/EMNLP on Interactive Demonstrations, pages 34– 35, Morristown, NJ, USA. Association for Computational Linguistics. Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005b. Recognizing contextual polarity in phrase-level sentiment analysis. In HLT-EMNLP ’05: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, pages 347– 354, Morristown, NJ, USA. Association for Computational Linguistics. Xiaoyun Wu and Rohini Srihari. 2004. Incorporating prior knowledge with weighted margin support vector machines. In KDD ’04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 326–333, New York, NY, USA. ACM. Omar F. Zaidan and Jason Eisner. 2008. Modeling annotators: a generative approach to learning from annotator rationales. In EMNLP ’08: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 31–40, Morristown, NJ, USA. Association for Computational Linguistics. Omar F. Zaidan, Jason Eisner, and Christine Piatko. 2007. Using “annotator rationales” to improve machine learning for text categorization. In NAACLHLT2007; Proceedings of the Main Conference, pages 260–267, April. 341