A vector space model for subjectivity classification in urdu aided by co-training

Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference

Volumn 2, Issue , 2010, Pages 860-868

  Mukund, Smruthi ^a   Srihari, Rohini K ^a  

^a State University of New York   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATIC CLASSIFIERS; CO-TRAINING; DATA SETS; LABELED DATA; SENTENCE LEVEL; VECTOR SPACE MODELS;

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL LINGUISTICS;

VECTOR SPACES;

EID: 80053425385     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. Agrawal R, Srikant R. 1994. Fast Algorithms for Mining Association Rules. In Proc. Of the Intl. Conf on Very Large databases. Santiago, Chile. Sept. Pp. 478-499.
2. Banea, C., Mihalcea, R., Wiebe, J., and Hassan, S. 2008. Multilingual subjectivity analysis using machine translation. In Proceedings of EMNLP-2008.
3. Banfield, A. 1982. Unspeakable Sentences. Routledge and Kegan Paul, Boston.
4. Blum, A. and Mitchell, T. 1998. Combining labeled and unlabeled data with co-training. Proceedings of the eleventh annual conference on Computational learning theory, ACM. p. 100.
5. Brank, J., Grobelnik, M., Milic-Frayling, N., and Mladenic, D. 2003. Training text classifiers with SVM on very few positive examples. Technical Report MSR-TR-2003-34, Microsoft Corp.
6. Chan, Philip K. and Stolfo J. Salvatore. 1998. Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection. Proc. 4th Int. Conf. on Knowledge Discovery and Data Mining (KDD-98), August 27-31, 1998, New York City, New York, USA, pp. 164-168. AAAI Press.
7. Danisman, T., and Alpkocak, A. 2008. Feeler: Emotion Classification of Text Using Vector Space Model. AISB 2008 Convention Communication, Interaction and Social Intelligence, p. 53.
8. Das, A., and Bandyopadhyay, S. 2009. Subjectivity Detection in English and Bengali: A CRF-based Approach. Seventh International Conference on Natural Language Processing (ICON 2009), December. Hyderabad, India.
9. Japkowicz Nathalie. 2000. Learning from Imbalanced Data Sets: A Comparison of Various Strategies. In Nathalie Japkowicz (ed.), Learning from Imbalanced Data Sets: Papers from the AAAI Workshop (Austin, Texas, Monday, July 31, 2000), AAAI Press, Technical Report WS-00-05, pp. 10-15.
10. Kennedy, A, & Inkpen, D. 2005. Sentiment classification of movie and product reviews using contextual valence shifters. In Workshop on the analysis of informal and formal information exchange during negotiations (FINEXIN 2005).
11. Ku, L. W., Liang, Y. T., and Chen, H. H. 2006. Opinion extraction, summarization and tracking in news and blog corpora. In Proceedings of AAAI-2006.
12. Kubat, Miroslav and Matwin Stan. 1997. Addressing the curse of imbalanced training sets: one-sided selection. Proc. 14th ICML, Nashville, Tennessee, USA, July 8-12, 1997, pp. 179-186.
13. Liu, B., Hu, M., and Cheng, J. 2005. Opinion observer: Analyzing and comparing opinions on the web. In Proceedings of WWW-2005.
14. Luo, N., Yuan, F., and Zuo, W. 2008. Using CoTraining and Semantic Feature Extraction for Positive and Unlabeled Text Classification. International Seminar on Future Information Technology and Management Engineering.
15. Mihalcea, R., Banea, C., and Wiebe, J. 2007. Learning multilingual subjective language via cross-lingual projections. In Proceedings of ACL-2007.
16. Mukund, S., and Srihari, R.K., 2009. NE Tagging for Urdu based on Bootstrap POS Learning. Third International Workshop on Cross Lingual Information Access: Addressing the Information Need of Multilingual Societies (CLIAWS3), NAACL - 2009, Boulder, CO.
17. Pang, B., Lee, L., and Vaithyanathan, S. 2002. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the Conference on EMNLP, pages 79-86.
18. Riloff, E., Wiebe, J., and Wilson, T. 2003. Learning subjective nouns using extraction pattern bootstrapping. Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4, Edmonton, Canada: Association for Computational Linguistics, pp. 25-32.
19. Soucy, P., and Mineau, G. W. 2005. Beyond tfidf weighting for text categorization in the vector space model. International Joint Conference on Artificial Intelligence, Cite-seer, p. 1130.
20. Su, F., and Markert K. 2008. From words to senses: a case study of subjectivity recognition. Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1, ACL, pp. 825-832.
21. Titov, I., and McDonald, R. 2008. A joint model of text and aspect ratings for sentiment summarization. In Proceedings of ACL-08: HLT.
22. Wan, X. 2008. Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis. In Proceedings of EMNLP-2008.
23. Wan, X. 2009. Co-Training for Cross-Lingual Sentiment Classification. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, Association for Computational Linguistics, pp. 235-243.
24. Wiebe, J. 1994. Tracking point of view in narrative. Computational Linguistics, 20(2):233-287.
25. th Annual Meeting of the Assoc. for Computational Linguistics (ACL-99).
26. th International Conference on Intelligent Text Processing and Computational Linguistics.
27. Wiebe, J., Wilson, T., and Cardie, C. 2005. Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, volume 39, issue 2-3, pp. 165-210. (Pubitemid 43320361)
28. Wilson, B. Edward. 1927. Probable Inference, the Law of Succession, and Statistical Inference. Journal of the American Statistical Association, Vol. 22, No. 158 (Jun., 1927), pp. 209-212.