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What Is the Basic Semantic Unit of Chinese Language? A Computational Approach Based on Topic Models

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The Mathematics of Language (MOL 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6878))

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Abstract

Chinese language has been generally regarded as a Subject-Verb -Object (SVO) language and the basic semantic unit is the Chinese word that is usually consisted by two or more Chinese characters. However, word-centered structure of Chinese language has been controversial in linguistics. Some recent research in computational linguistics in Chinese language suggests that the character-based models perform better than the word-based models in some applications such word segmentation. In this paper, the word-based topic models and the character-based models are tested for modeling Chinese language, respectively. By empirical studies, we demonstrated the effectiveness of using Chinese characters as the basic semantic units. These two models have close performance in text classifications while the character-based model has a better quality in language modeling and a much smaller vocabulary. By testing on a bilingual corpus, three independent topic models based on Chinese words, Chinese characters and English words are trained and compared to each other. we verify the capability of topic models in modeling semantics by experiments across Chinese and English. The classification accuracy can also be boosted up by aggregating the classification results from the three independent topic models.

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References

  1. Barbara, A.: The Nature of the Chinese Character. Simon, New York (1991)

    Google Scholar 

  2. Bishop, M.C.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  3. Blei, D.M., Griffiths, T., Jordan, M.I., Tenenbaum, J.: Hierarchical Topic Models and the Nested Chinese Restaurant Process. In: Thrun, S., Saul, L., Schoelkopf, B. (eds.) Advances in Neural Information Processing Systems (2004)

    Google Scholar 

  4. Blei, D.M., Lafferty, J.D.: Correlated Topic Models. In: Advances in Neural Information Processing Systems, vol. 18. MIT Press, Cambridge (2006)

    Google Scholar 

  5. Blei, D.M., Lafferty, J.D.: Dynamic Topic Model. In: Proceedings of the 23rd ICML, Pittsburgh, USA (2006)

    Google Scholar 

  6. Blei, D.M., Ng, A., Jordan, M.I.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993–1022 (2003)

    MATH  Google Scholar 

  7. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. Journal of the American Soc. of Inform. Sci. 41 (1990)

    Google Scholar 

  8. Griffiths, T.L., Steyvers, M.: Finding Scientific Topics. Proceedings of the National Academy of Science 101, 5228–5235 (2004)

    Article  Google Scholar 

  9. Griffiths, T.L., Steyvers, M., Blei, D.M., Tenenbaum, J.B.: Integrating topics and syntax. In: Advances in Neural Information Processing Systems, vol. 17 (2005)

    Google Scholar 

  10. Hofmann, T.: Probabilistic Latent Semantic Analysis. In: Proceedings of UAI 1999, Stockholm (1999)

    Google Scholar 

  11. Huang, Z., Thint, M., Qin, Z.: Question Classification using Head Words and their Hypernyms. In: Proceedings of EMNLP, pp. 927–936 (2008)

    Google Scholar 

  12. Li, C., Sandra, T.: Mandarin Chinese: A Functional Reference Grammar. University of California Press, Los Angeles (1981) ISBN 978-0520066106

    Google Scholar 

  13. Manning, C., Schutze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  14. Maurits, L., Perfors, A., Navarro, D.: Why are some word orders more common than others? A uniform information density account. In: Proceedings of NIPS (2010)

    Google Scholar 

  15. Minka, T., Lafferty, J.: Expectation-propagation for the generative aspect model. In: Uncertainty in Artificial Intelligence (2002)

    Google Scholar 

  16. Ng, H.T., Low, J.K.: Chinese part-of-speech tagging: one-at-a-time or all-at- once? word-based or character-based. In: Proceedings of EMNLP, pp. 277–284 (2004)

    Google Scholar 

  17. Qin, Z., Thint, M., Huang, Z.: Ranking Answers by Hierarchical Topic Models. In: Chien, B.-C., Hong, T.-P., Chen, S.-M., Ali, M. (eds.) IEA/AIE 2009. LNCS, vol. 5579, pp. 103–112. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  18. Steyvers, M., Griffiths, T.: Probabilistic topic models. In: Landauer, T., McNamara, D., Dennis, S., Kintsch, W. (eds.) Latent Semantic Analysis - A Road to Meaning (2007)

    Google Scholar 

  19. Wang, K., Zong, C., Su, K.-Y.: A character-based joint model for Chinese word segmentation. In: Proceedings of CoLing, pp. 1173–1181 (2010)

    Google Scholar 

  20. Wei, X., Croft, W.B.: LDA-based document models for ad-hoc retrieval. In: SIGIR (2006)

    Google Scholar 

  21. Wu, Y., Ding, Y., Wang, X., Xu, J.: A comparative study of topic models for topic clustering of Chinese web news. Computer Science and Information Technology (ICCSIT) 5, 236–240 (2010)

    Google Scholar 

  22. Xu, T.Q.: Fundamental structural principles of Chinese semantic syntax in terms of Chinese Characters. Applied Linguistics 1, 3–13 (2001) (In Chinese)

    Google Scholar 

  23. Zhang, Y., Qin, Z.: A topic model of Observing Chinese Characters. In: Proceedings of the 2nd International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), pp. 7–10 (2010)

    Google Scholar 

  24. http://www.en.wikipedia.org/wiki/Chinese_language

  25. http://www.en.wikipedia.org/wiki/Subject_Verb_Object

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Zhao, Q., Qin, Z., Wan, T. (2011). What Is the Basic Semantic Unit of Chinese Language? A Computational Approach Based on Topic Models. In: Kanazawa, M., Kornai, A., Kracht, M., Seki, H. (eds) The Mathematics of Language. MOL 2011. Lecture Notes in Computer Science(), vol 6878. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23211-4_9

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  • DOI: https://doi.org/10.1007/978-3-642-23211-4_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23210-7

  • Online ISBN: 978-3-642-23211-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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