Twitter part-of-speech tagging using pre-classification hidden Markov model

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Volumn , Issue , 2012, Pages 1118-1123

  Sun, Shichang ^a,b   Liu, Hongbo ^a   Lin, Hongfei ^a   Abraham, Ajith ^c,d  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

^b DALIAN NATIONALITIES UNIVERSITY   (China)

^c VSB TECHNICAL UNIVERSITY OF OSTRAVA   (Czech Republic)

^d SCIENTIFIC NETWORK FOR INNOVATION AND RESEARCH EXCELLENCE   (United States)

Author keywords

Hidden Markov models; Naive Bayes model; Part of speech tagging; Subjectivity analysis

Indexed keywords

DATA SETS; GLOBAL KNOWLEDGE; HMM MODELS; NAIVE BAYES MODELS; NATURAL LANGUAGE PROCESSING; PART OF SPEECH TAGGING; POS TAGGING; RESEARCH TOPICS; SENTIMENT ANALYSIS; SEQUENCE RECOGNITION; SUBJECTIVITY ANALYSIS;

CYBERNETICS; NATURAL LANGUAGE PROCESSING SYSTEMS; SYNTACTICS;

HIDDEN MARKOV MODELS;

EID: 84872417810     PISSN: 1062922X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICSMC.2012.6377881     Document Type: Conference Paper

References (25)

1. M. Chang, D. Goldwasser, D. Roth, and V. Srikumar, "Structured output learning with indirect supervision," in Proceedings of the International Conference on Machine Learning. IEEE Computer Society, 2010, pp. 199-206.
2. F. Salvetti, S. Lewis, and C. Reichenbach, "Impact of lexical filtering on overall opinion polarity identification," in Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications, Stanford, US, 2004.
3. K. Gimpel, N. Schneider, B. O. Connor, D. Das, D. Mills, J. Eisenstein, M. Heilman, D. Yogatama, J. Flanigan, and N. A. Smith, "Part-of-Speech Tagging for Twitter : Annotation, Features, and Experiments," in Proceedings of the Annual Meeting of the Association for Computational Linguistics, companion volume, 2011.
4. F. Ji, Z. Liu, X. Qiu, and X. Huang, "Part-of-speech tagging for micro blog via 2d sequence labeling," Journal of Computational Information Systems, vol. 8, no. 3, pp. 1149-1156, 2012.
5. J. Kim, H. Rim, and J. Tsujii, "Self-organizing markov models and their application to part-of-speech tagging," in Proceedings of the 41st Annual Meeting on Association for Computational Linguistics-Volume 1. Association for Computational Linguistics, 2003, pp. 296-302.
6. G. Zhou and J. Su, "Error-driven hmm-based chunk tagger with context-dependent lexicon," in Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2000, pp. 71-79.
7. D. Freitag and A. McCallum, "Information extraction with hmm structures learned by stochastic optimization," in Proceedings of the National Conference on Artificial Intelligence. Menlo Park, CA, 2000, pp. 584-589.
8. T. L. Griffiths, M. Steyvers, D. M. Blei, and J. B. Tenenbaum, "Integrating topics and syntax," in In Advances in Neural Information Processing Systems 17. MIT Press, 2005, pp. 537-544.
9. J. Meng, H. Lin, and Y. Yu, "A two-stage feature selection method for text categorization," Computers and Mathematics with Applications, vol. 62, no. 7, pp. 2793-2800, 2011.
10. J. Li, J. Wang, Y. Zhao, and Z. Yang, "Self-adaptive design of hidden markov models," Pattern Recognition Letters, vol. 25, no. 2, pp. 197-210, 2004. (Pubitemid 38003131)
11. M. Johnson, "Why doesnt em find good hmm pos-taggers," in In EMNLP, 2007, pp. 296-305.
12. N. Najkar, F. Razzazi, and H. Sameti, "A novel approach to hmm-based speech recognition systems using particle swarm optimization," Mathematical and Computer Modelling, vol. 52, no. 11-12, pp. 1910-1920, 2010.
13. J. Meng, S. Xu, X. Wang, Y. Yi, and H. Liu, "Swarm-based dhmm training and application in time sequences classification," Journal of Computational Information Systems, vol. 1, pp. 197-203, 2010.
14. T. Chen, X. Mei, J. Pan, and S. Sun, "Optimization of hmm by the tabu search algorithm," Journal of Information science and engineering, vol. 20, no. 5, pp. 949-957, 2004.
15. Y. Cheng and S. Li, "Fuzzy time series forecasting with a probabilistic smoothing hidden markov model," Fuzzy Systems, IEEE Transactions on, vol. 20, no. 2, pp. 291-304, 2012.
16. M. Rafiul Hassan, B. Nath, M. Kirley, and J. Kamruzzaman, "A hybrid of multiobjective Evolutionary Algorithm and HMM-Fuzzy model for time series prediction," Neurocomputing, vol. 81, pp. 1-11, Apr. 2012.
17. J. Zeng and Z. Liu, "Type-2 fuzzy hidden markov models and their application to speech recognition," IEEE Transactions on Fuzzy Systems, vol. 14, no. 3, pp. 454-467, 2006. (Pubitemid 43898887)
18. A. McCallum, D. Freitag, and F. Pereira, "Maximum entropy markov models for information extraction and segmentation," in Proceedings of the Seventeenth International Conference on Machine Learning. IEEE, 2000, pp. 591-598.
19. J. Lafferty, A. McCallum, and F. Pereira, "Conditional random fields: Probabilistic models for segmenting and labeling sequence data," in Proceedings of the Eightteenth International Conference on Machine Learning. IEEE, 2001, pp. 282-289.
20. T. Brants, "Tnt: a statistical part-of-speech tagger," in Proceedings of the Sixth Conference on Applied Natural Language Processing. Association for Computational Linguistics, 2000, pp. 224-231.
21. L. Rabiner, "A tutorial on hidden markov models and selected applications in speech recognition," Proceedings of the IEEE, vol. 77, no. 2, pp. 257-286, 1989.
22. B. Juang and L. Rabiner, Fundamentals of speech recognition. Prentic-Hall, Englewood Cliffs, NJ, 1993.
23. L. Baum and T. Petrie, "Statistical inference for probabilistic functions of finite state markov chains," The Annals of Mathematical Statistics, vol. 37, no. 6, pp. 1554-1563, 1966.
24. L. Baum and J. Eagon, "An inequality with applications to statistical estimation for probabilistic functions of markov processes and to a model for ecology," Bulletin of American Mathematical Society, vol. 73, no. 3, pp. 360-363, 1967.
25. E. Loper and S. Bird, "NLTK: The Natural Language Toolkit," in Proceedings of the ACL Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics. Somerset, NJ: Association for Computational Linguistics, 2002, pp. 62-69.