The Menzerath–Altmann law: Experimenting with Tatar texts

A.M. Galieva

Kazan Federal University, Kazan, 420008 Russia

E-mail: amgalieva@gmail.com

Received December 28, 2020

ORIGINAL ARTICLE

Full text PDF
DOI: 10.26907/2541-7738.2021.1.180-189

For citation: Galieva A.M. The Menzerath–Altmann law: Experimenting with Tatar texts. Uchenye Zapiski Kazanskogo Universiteta. Seriya Gumanitarnye Nauki, 2021, vol. 163, no. 1, pp. 180–189. doi: 10.26907/2541-7738.2021.1.180-189. (In Russian)

Abstract

The Menzerath–Altmann law on the relationship between the length of linguistic units and the length of their components is one of the important laws of quantitative linguistics. This law is a result of an advanced linguistic structures organization and is of great importance for the modern theory of language aimed at revealing the relations between qualitative features and quantitative parameters of the language.

The validity of the Menzerath–Altmann law has been confirmed in a number of works on languages with different morphological structures. The main purpose of this paper is empirical testing of the Menzerath–Altmann law on the Tatar language with the help of various fiction texts (both poetry and prose).

The distribution of word forms in the Tatar language by length, observed values of the average syllable length depending on the word length, average values of the syllable length predicted by the model, as well as the model parameters were investigated for the analyzed texts. To assess the goodness of fitting of the model, the coefficient of determination R², which for different texts ranged from 0.676 to 0.999, was used. It was concluded that G. Altman’s formula is in good agreement with the data of the Tatar language. The model predicts not only the decreasing average syllable length with the increasing word length (function monotonicity), but also its subsequent increasing (change in the function monotonicity) for a number of texts.

Keywords: word length, syllable length, quantitative linguistics, Menzerath–Altmann law, modeling of length of linguistic units, Tatar language

Acknowledgments. The work is performed according to the Russian Government Program of Competitive Growth of Kazan Federal University.

Figure Captions

Fig. 1. Distribution of words in the Tatar text by length (the length of word forms is measured in phonemes).

Fig. 2. Distribution of words in the Tatar text by length (the length of word forms is measured in syllables).

Fig. 3. Distribution of words in the text depending on the average syllable length.

Fig. 4. Observed and predicted values of the average syllable length in G. Ibragimova’s text.

References

1. Altmann G. Aspects of word length. In: Köhler R., Altmann G. (Eds.). Issues in Quantitative Linguistics. Vol. 3. Lüdenscheid, RAM-Verlag, 2013, pp. 23–38.
2. Popescu I., Naumann S., Kelih E., Rovenchak A., Overbeck A., Sanada H., Smith R., Čech R., Mohanty P., Wilson A., Altmann G. Word length: Aspects and languages. In: Altmann G., Köhler R. (Eds.). Issues in Quantitative Linguistics. Vol. 3. Lüdenscheid, RAM-Verlag, 2013, pp. 224–281.
3. Grzybek P. History and methodology of word length studies. In: Contributions to the Science of Text and Language. Word Length Studies and Related Issues. Heidelberg, Springer, 2005, pp. 15–90.
4. Rizvanova L.M. Quantitative features of the Tatar word (based on the material of certain functional styles in the Tatar language). Extended Abstract of Cand. Philol. Diss. Kazan, 1996. 24 p. (In Russian)
5. Galieva A.M. Word length in Tatar: Selecting relevant parameters for modeling. Computational Models in Language and Speech: Proc. Comput. Models Lang. Speech Workshop (CMLS 2020) Co-Located with 16th Int. Conf. on Comput. Cognit. Linguist. (TEL 2020). Kazan, Russia, Nov. 12–13, 2020. CEUR Workshop Proc. Elizarov A., Loukachevitch N. (Eds.), 2020, vol. 2780, pp. 179–186.
6. Kromer V. About word length distribution. In: Contributions to the Science of Text and Language. Word Length Studies and Related Issues. Heidelberg, Springer, 2005, pp. 199–210.
7. Altmann G. Prolegomena to Menzerath’s law. Glottometrika, 1980, no. 2, pp. 1–10.
8. Fenk A., Fenk-Oczlon G., Fenk L. Syllable complexity as a function of word complexity. Proc. VIII Int. Conf. “Cognitive Modeling in Linguistics”, 2006, vol. 1, pp. 324–333.
9. Fenk A., Fenk-Oczlon G. Menzerath’s law and the constant flow of linguistic information. Contributions to Quantitative Linguistics: Proc. 1st Int. Conf. on Quant. Linguist., QUALICO, Trier, 1991. Springer, 1993, pp. 11–31.
10. Xu L., He L. Is the Menzerath–Altmann law specific to certain languages in certain registers? Journal of Quantitative Linguistics, 2020, vol. 27, no. 3, pp. 187–203. doi: 10.1080/09296174.2018.1532158.
11. Mačutek J., Chromý J., Koščová M. Menzerath–Altmann law and prothetic /v/ in spoken Czech. Journal of Quantitative Linguistics, 2019, vol. 26, no. 1, pp. 66–80. doi: 10.1080/09296174.2018.1424493.
12. Hou R., Huang C.-R., Ahrens K., Lee Y.-M. Linguistic characteristics of Chinese register based on the Menzerath–Altmann law and text clustering. Digital Scholarship in the Humanities, 2020, vol. 35, no. 1, pp. 54–66. doi: 10.1093/llc/fqz005.
13. Cramer I. The parameters of the Altmann–Menzerath law. Journal of Quantitative Linguistics, 2005, vol. 12, no. 1, pp. 41–52. doi: 10.1080/09296170500055301.
14. Milička J. Menzerath’s law: The whole is greater than the sum of its parts. Journal of Quantitative Linguistics, 2014, vol. 21, no. 2, pp. 85–99. doi: 10.1080/09296174.2014.882187.
15. Kułacka A., Mačutek J. A discrete formula for the Menzerath–Altmann law. Journal of Quantitative Linguistics, 2007, vol. 14, no. 1, pp. 23–32. doi: 10.1080/09296170600850585.
16. Gustison M.L., Semple S., Ferrer-i-Cancho R., Bergman T.J. Gelada vocal sequences follow Menzerath’s linguistic law. Proceedings of the National Academy of Sciences of the United States of America, 2016, vol. 113, no. 19, pp. E2750–E2758. doi: 10.1073%2Fpnas.1522072113.
17. Li W. Menzerath’s law at the gene-exon level in the human genome. Complexity, 2012, vol. 17, no. 4, pp. 49–53. doi: 10.1002/cplx.20398.
18. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, 2018. Available at: https://www.R-project.org/.

The content is available under the license Creative Commons Attribution 4.0 License.