Abstract
This paper presents a novel way to learn Chinese polarity lexicons by using both external relations and internal formation of Chinese words, i.e. by integrating two kinds of different but complementary models: graph models and morphological feature-based models. The polarity detection is first treated as a semi-supervised learning in a graph, and then machine learning is used based on morphological features of Chinese words. The results show that the the integration of morphological feature-based models and graph models significantly outperforms the baselines.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Brin, S., Page, L.: The Anatomy of a Large-scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems 30(1-7), 107–117 (1998)
Esuli, A., Sebastiani, F.: PageRanking WordNet SynSet: An application to opinion mining. In: Proceedings of ACL, pp. 424–431 (2007)
Hatzivassiloglou, V., McKeown, K.: Predicting the Semantic Orientation of Adjectives. In: Proceedings of ACL1997. pp. 174–181 (1997)
Kamps, J., Marx, M.: Words with Attitude. In: Proceedings of the First International Conference on Global WordNet, pp. 332–341 (2002)
Ku, L.W., Chen, H.H.: Mining Opinions from the Web: Beyond Relevance Retrieval. Journal of American Society for Information Science and Technology, Special Issue on Mining Web Resources for Enhancing Information Retrieval 58(12), 1838–1850 (2007)
Ku, L.W., Huang, T.H., Chen, H.H.: Using Morphological and Syntactic Structures for Chinese Opinion Analysis. In: Proceedings of EMNLP, pp. 1260–1269 (2009)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of EMNLP, pp. 79–86 (2002)
Polikar, R.: Ensemble Based Systems in Decision Making. IEEE Circuits and Systems Magazine 6(3), 21–45 (2006)
Rao, D., Ravichandran, D.: Semi-supervised Polarity Lexicon Induction. In: Proceedings of EACL, pp. 675–682 (2009)
Rao, D., Yarowsky, D.: Ranking and Semi-supervised Classification on Large Scale Graphs Using Map-Reduce. In: Proceedings of Textgraphs-4, pp. 58–69 (2009)
Riloff, E., Wiebe, J.: Learning Extraction Patterns for Subjective Expressions. In: Proceedings of EMNLP, pp. 105–112 (2003)
Mei, J., Zhu, Y., Gao, Y., Yin, H.: Tongyici Cilin(2nd version). Shanghai CiShu Press (1996) (in Chinese)
Shi, J., Zhu, Y.: The Lexicon of Chinese Positive Words. Sichuan Lexicon Press (2006) (in Chinese)
Turney, P.D., Littman, M.L.: Measuring Praise and Criticism: Inference of semantic orientation from association. ACM Trans. On Information Systems 21(4), 315–346 (2003)
Velikovich, L., Blair-Goldensohn, S., Hannan, K., McDonald, R.: The Viability of Web-derived Polarity Lexicons. In: Proceedings of NAACL, pp. 777–785 (2010)
Yuen, R.W.M., Chan, T.Y.W., Lai, T.B.Y., Kwong, O.Y., Tsou, B.K.Y.: Morpheme-based Derivation of Bipolar Semantic Orientation of Chinese Words. In: Proceedings of COLING, pp. 1008–1014 (2004)
Zhu, X., Ghahramani, Z.: Learning from labeled and unlabeled data with Label Propagation. Technical Report CMU-CALD-02-107, CarnegieMellon University (2002)
Zhu, Y., Min, J., Zhou, Y., Huang, X., Wu, L.: Semantic Orientation Computing Based on HowNet. Journal of Chinese Information Processing 20(1), 14–20 (2006) (in Chinese)
Zhu, L., Zhu, Y.: The Lexicon of Chinese Negative Words. Sichuan Lexicon Press (2006) (in Chinese)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lu, B., Song, Y., Zhang, X., Tsou, B.K. (2010). Learning Chinese Polarity Lexicons by Integration of Graph Models and Morphological Features. In: Cheng, PJ., Kan, MY., Lam, W., Nakov, P. (eds) Information Retrieval Technology. AIRS 2010. Lecture Notes in Computer Science, vol 6458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17187-1_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-17187-1_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17186-4
Online ISBN: 978-3-642-17187-1
eBook Packages: Computer ScienceComputer Science (R0)