Abstract
Exploring linguistic features and characteristics helps better understand natural language. Recently, there have been many studies on the internal relationships of linguistic features, such as collocation of morphemes, words, or phrases. Although they have drawn many useful conclusions, some summarized linguistic rules lack physical verification of large-scale data. Due to the development of machine learning theories, we are now able to use computer technologies to process massive corpus automatically. In this paper, we reveal a new methodology to conduct linguistic research, in which machine learning algorithms help extract the syntactic structures and mine their intrinsic relationships. Not only the association of parts of speech (POS), but also the positive and negative correlations of syntactic structures, linear and nonlinear correlation are considered, which have not been well studied before. Combined with the linguistic theory, detailed analyses show that the association between parts of speech and syntactic structures mined by machine learning method has an excellent stylistic explanatory effect.
This work is supported by 2018 National Major Program of Philosophy and Social Science Fund “Analyses and Researches of Classic Texts of Classical Literature Based on Big Data Technology” (18ZDA238) and Project of Humanities and Social Sciences of Ministry of Education in China (17YJAZH056).
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References
Allen, J.F.: Towards a general theory of action and time. Artif. Intell. 23(2), 123–154 (1984)
Benesty, J., Chen, J., Huang, Y., Cohen, I.: Pearson correlation coefficient. In: Cohen, I., Huang, Y., Chen, J., Benesty, J. (eds.) Noise Reduction in Speech Processing, pp. 1–4. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00296-0
Bernstein, J.B.: Topics in the syntax of nominal structure across romance (1994)
Chang, H.W.: The acquisition of Chinese syntax. In: Advances in Psychology, vol. 90, pp. 277–311. Elsevier (1992)
Chomsky, N.: Aspects of the Theory of Syntax, vol. 11. MIT Press, Cambridge (2014)
Zhu, D.: Grammar Printed Lecture. Commercial Press, Beijing (1982)
Ferguson, C.A.: Dialect, register, and genre: working assumptions about conventionalization. In: Sociolinguistic Perspectives on Register, pp. 15–30 (1994)
Firth, J.R.: Papers in Linguistics 1934-1951: Repr. Oxford University Press (1961)
Foley, W.A., et al.: Functional Syntax and Universal Grammar. Cambridge University Press, Cambridge (2009)
Li, K., Man, H., et al.: Boundedness of VP and linked event structure. Ph.D. thesis (2013)
Langfelder, P., Zhang, B., Horvath, S.: Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for R. Bioinformatics 24(5), 719–720 (2007)
Pollock, J.Y.: Verb movement, universal grammar, and the structure of IP. Linguist. Inq. 20(3), 365–424 (1989)
Rorat, T.: Plant dehydrins–tissue location, structure and function. Cell. Mol. Biol. Lett. 11(4), 536 (2006)
Seretan, V.: Induction of syntactic collocation patterns from generic syntactic relations (2005)
Seretan, V.: Collocation extraction based on syntactic parsing. Ph.D. thesis, University of Geneva (2008)
Seretan, V.: Syntax-Based Collocation Extraction, vol. 44. Springer, Heidelberg (2011). https://doi.org/10.1007/978-94-007-0134-2
Seretan, V., Wehrli, E.: Accurate collocation extraction using a multilingual parser. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, pp. 953–960. Association for Computational Linguistics (2006)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc.: Ser. B (Methodol.) 58(1), 267–288 (1996)
Watson, D., Tellegen, A.: Toward a consensual structure of mood. Psychol. Bull. 98(2), 219 (1985)
Zhu, W.: A study on the syntactic structure of the study of word collocation variation in “red sorghum”. Chin. Teach. 5, 153–155 (2014)
Wright, W.: Lectures on the Comparative Grammar of the Semitic Languages, vol. 43. University Press (1890)
Xia, F.: The part-of-speech tagging guidelines for the Penn Chinese Treebank (3.0). IRCS Technical reports Series, p. 38 (2000)
Fan, X.: Sentence meaning. Chin. Linguist. 3, 2–12 (2010)
Huo, X.L.: Research on stylistic features and its influence variables. Master’s thesis, Nanjing University (2014)
Xue, N., Xia, F., Chiou, F.D., Palmer, M.: The penn chinese TreeBank: phrase structure annotation of a large corpus. Natural language engineering 11(2), 207–238 (2005)
Yip, M.J.: The tonal phonology of Chinese. Ph.D. thesis, Massachusetts Institute of Technology (1980)
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Wu, H., Liu, Y. (2019). Association Relationship Analyses of Stylistic Syntactic Structures. In: Sun, M., Huang, X., Ji, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics. CCL 2019. Lecture Notes in Computer Science(), vol 11856. Springer, Cham. https://doi.org/10.1007/978-3-030-32381-3_4
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