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12 - Factors Influencing Dependency Distance

An Account of the MDD Variation between Chinese and English

Published online by Cambridge University Press:  19 December 2024

By
Ruochen Niu  and
Haitao Liu
Edited by
Eva Duran Eppler ,
Nikolas Gisborne  and
Andrew Rosta
Eva Duran Eppler
Affiliation:
Roehampton University, London
Nikolas Gisborne
Affiliation:
University of Edinburgh
Andrew Rosta
Affiliation:
University of Central Lancashire, Preston
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Summary

Dependency distance (DD) is a syntactic complexity measure proposed by Hudson (). With the development of annotated corpora, researchers have become able to examine the relation between DD and processing difficulty using large-scale data. Based on treebanks of twenty languages, Liu () identified the universal later called “dependency distance minimization”. While the mean DD of languages tends to be minimized as constrained by working memory, there also seem to be considerable differences among languages, particularly between Chinese and English (Hudson, ). To investigate whether this variation is induced by corpus-based factors (such as sentence length, genre and annotation scheme) or by deeper motivations (such as syntactic differences of languages), a series of studies were conducted. Their results indicate that while shorter sentences, informative and spoken texts, as well as annotations based on syntactic functions may contribute to shorter DDs, the DD variation between Chinese and English is more likely the result of different syntactic structures and processing mechanisms (such as the use of words rather than affixes to express tense in Chinese).

Keywords

dependency distancecross-linguistic similarities and differencescognitive constraintssyntactic structures
Type
Chapter
Information
Word Grammar, Cognition and Dependency , pp. 276 - 296
Publisher: Cambridge University Press
Print publication year: 2025

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References

Cowan, N. (2000). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87–114.CrossRefGoogle Scholar
Duran Eppler, Eva. (2010). Emigranto: The syntax of German/English code-switching. Vienna: Braumueller.Google Scholar
Eppler, E. M. (2005). The syntax of German-English code-switching. Doctoral dissertation, University College London.Google Scholar
Eppler, E. D. & Wang, L. (2014). The role of dependency distance in German/English and Chinese/English processing. In Casesnoves-Ferrer, R., Forcadell, M. & Ferré, N. G. (eds.), Ens queda la paraula. Estudis de lingüística aplicada en honor a M. Teresa Turell. Barcelona: Institut Universitari de Lingüística Aplicada (IULA), Universitat Pompeu Fabra. 173–96.Google Scholar
Ferrer-i-Cancho, R. (2004). Euclidean distance between syntactically linked words. Physical Review E, 70(5), 056135.CrossRefGoogle ScholarPubMed
Ferrer-i-Cancho, R. (2013). Hubiness, length, crossings and their relationships in dependency trees. Glottometrics, 25, 1–21.Google Scholar
Ferrer-i-Cancho, R. & Liu, H. (2014). The risks of mixing dependency lengths from sequences of different length. Glottotheory, 5(2), 143–55.CrossRefGoogle Scholar
Ferrer-i-Cancho, R., Gómez-Rodríguez, C., Esteban, J. L. & Alemany-Puig, L. (2022). Optimality of syntactic dependency distances. Physical Review E, 105(1), 014308.CrossRefGoogle ScholarPubMed
Frazier, L. (1979). On comprehending sentences: Syntactic parsing strategies. Doctoral dissertation, University of Connecticut.Google Scholar
Friederici, A. D. (1995). The time course of syntactic activation during language processing: A model based on neuropsychological and neurophysiological data. Brain and Language, 50(3), 259–81.CrossRefGoogle Scholar
Futrell, R., Mahowald, K. & Gibson, E. (2015). Large-scale evidence of dependency length minimization in 37 languages. Proceedings of the National Academy of Sciences (PNAS), 112(33), 10336–41.CrossRefGoogle ScholarPubMed
Gibson, E. (1998). Linguistic complexity: Locality of syntactic dependencies. Cognition, 68(1), 1–76.CrossRefGoogle ScholarPubMed
Gibson, E. (2000). The dependency locality theory: A distance-based theory of linguistic complexity. In Marantz, A. P., Miyashita, Y. & O’Neil, W. (eds.), Image, language, brain: Papers from the first mind articulation project symposium. Cambridge, MA: The MIT Press. 95–126.Google Scholar
Gildea, D. & Temperley, D. (2010). Do grammars minimize dependency length? Cognitive Science, 34(2), 286–310.CrossRefGoogle ScholarPubMed
Grzybek, P. (2013). Homogeneity and heterogeneity within language(s) and text(s): Theory and practice of word length modeling. Issues in Quantitative Linguistics, 3, 66–99.Google Scholar
Hawkins, J. A. (1994). A performance theory of order and constituency. Cambridge: Cambridge University Press.Google Scholar
Hawkins, J. A. (2004). Efficiency and complexity in grammars. Oxford: Oxford University Press.CrossRefGoogle Scholar
Heringer, H. J., Strecker, B. & Wimmer, R. (1980). Syntax: Fragen, Lösungen, Alternativen. München: Wilhelm Fink Verlag.Google Scholar
Hiranuma, S. (1999). Syntactic difficulty in English and Japanese: A textual study. UCL Working Papers of Linguistics (UCLWPL), 11, 309–22.Google Scholar
Hudson, R. (1995). Measuring syntactic difficulty. http://dickhudson.com/wp-content/uploads/2013/07/Difficulty.pdf (accessed December 15, 2018).Google Scholar
Hudson, R. (1998). Word Grammar. Unpublished manuscript. https://bit.ly/3Q8Ksdl (accessed April 19, 2024).Google Scholar
Hudson, R. (2009). Foreword. In Liu, H., Yīcún yǚfǎ de lǐlùn yǚ shíjiàn [Dependency grammar: From theory to practice]. Beijing: Science Press. vii–x.Google Scholar
Hudson, R. (2010). An introduction to Word Grammar. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Hudson, R. (2017). Cross-language diversity, head-direction and grammars. Comment on “Dependency distance: A new perspective on syntactic patterns in natural languages” by H. Liu et al. Physics of Life Reviews, 21, 204–6.Google ScholarPubMed
Jiang, J. & Liu, H. (2015). The effects of sentence length on dependency distance, dependency direction and the implications – based on a parallel English–Chinese dependency treebank. Language Sciences, 50, 93–104.CrossRefGoogle Scholar
Kromann, M. T. (2005). Discontinuous Grammar: A dependency-based model of human parsing and language learning. Doctoral dissertation, Copenhagen Business School.Google Scholar
Li, W. (2012). Jīyú yīcún shùkù de yīnghàn jùfǎ duìbǐ yánjiū [Contrastive study of syntax in Chinese and English based on dependency treebank]. Doctoral dissertation, Communication University of China, Beijing.Google Scholar
Liu, H. (2007). Probability distribution of dependency distance. Glottometrics, 15, 1–12.Google Scholar
Liu, H. (2008). Dependency distance as a metric of language comprehension difficulty. Journal of Cognitive Science, 9(2), 159–91.Google Scholar
Liu, H. (2009). Yīcún yǚfǎ de lǐlùn yǚ shíjiàn [Dependency grammar: From theory to practice]. Beijing: Science Press.Google Scholar
Liu, H. (2018). Language as a human-driven complex adaptive system. Physics of Life Reviews, 26–27, 149–51.Google ScholarPubMed
Liu, H. & Cong, J. (2014). Empirical characterization of modern Chinese as a multi-level system from the complex network approach. Journal of Chinese Linguistics, 42(1), 1–38.Google Scholar
Liu, H., Hudson, R. & Feng, Z. (2009a). Using a Chinese treebank to measure dependency distance. Corpus Linguistics and Linguistic Theory, 5(2), 161–74.CrossRefGoogle Scholar
Liu, H., Zhao, Y. & Li, W. (2009b). Chinese syntactic and typological properties based on dependency syntactic treebanks. Poznań Studies in Contemporary Linguistics, 45(4), 495–509.CrossRefGoogle Scholar
Liu, H., Xu, C. & Liang, J. (2017). Dependency distance: A new perspective on syntactic patterns in natural languages. Physics of Life Reviews, 21, 171–93.CrossRefGoogle ScholarPubMed
Lu, Q. & Liu, H. (2016a). Yīcún jùlí fēnbù yoǔ guīlǜ ma [Does dependency distance distribute regularly]? Journal of Zhejiang University (Humanities and Social Sciences), 46(4), 63–76.Google Scholar
Lu, Q. & Liu, H. (2016b). Rénlèi yǚyán zhōng jiāochā yǚ jùlí guānxi de jìliáng fēnxī [A quantitative study of the relationship between crossing and distance of human language]. Journal of Shanxi University (Philosophy and Social Sciences), 39(4), 49–56.Google Scholar
Lu, Q., Lin, Y. & Liu, H. (2018). Dynamic valency and dependency distance. In Jiang, J. & Liu, H. (eds.), Quantitative analysis of dependency structures. Berlin: De Gruyter Mouton. 145–66.Google Scholar
Lu, Q., Xu, C. & Liu, H. (2016). Can chunking reduce syntactic complexity of natural languages? Complexity, 21(S2), 33–41.CrossRefGoogle Scholar
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81–97.CrossRefGoogle ScholarPubMed
Niu, R. & Liu, H. (2022). Effects of syntactic distance and word order on language processing: An investigation based on a psycholinguistic treebank of English. Journal of Psycholinguistic Research, 51(5), 1043–62.CrossRefGoogle ScholarPubMed
Nivre, J., de Marneffe, M.-C., Ginter, F., Goldberg, Y., Hajič, J., Manning, C. D., … Zeman, D. (2016). Universal Dependencies v1: A multilingual treebank collection. Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC’16), 1659–66.Google Scholar
Nivre, J., de Marneffe, M.-C., Ginter, F., Hajič, J., Manning, C. D., Pyysalo, S., … Zeman, D. (2020). Universal Dependencies v2: An evergrowing multilingual treebank collection. Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), 4034–43.Google Scholar
Osborne, T. J. (2019). A dependency grammar of English. An introduction and beyond. Amsterdam: John Benjamins.CrossRefGoogle Scholar
Osborne, T. & Gerdes, K. (2019). The status of function words in dependency grammar: A critique of Universal Dependencies (UD). Glossa, 4(1), 1–28.Google Scholar
Temperley, D. (2007). Minimization of dependency length in written English. Cognition, 105(2), 300–33.CrossRefGoogle ScholarPubMed
Tesnière, L. (1959). Elements of structural syntax. Paris: Klincksieck.Google Scholar
Ueno, M. & Polinsky, M. (2009). Does headedness affect processing? A new look at the VO–OV contrast. Journal of Linguistics, 45(3), 675–710.CrossRefGoogle Scholar
Wang, L. & Liu, H. (2013). Syntactic variations in Chinese–English code-switching. Lingua, 123, 58–73.CrossRefGoogle Scholar
Wang, L. & Liu, H. (2016). Syntactic differences of adverbials and attributives in Chinese-English code-switching. Language Sciences, 55, 16–35.CrossRefGoogle Scholar
Wang, Y. & Liu, H. (2017). The effects of genre on dependency distance and dependency direction. Language Sciences, 59, 135–47.CrossRefGoogle Scholar
Xu, C. & Liu, H. (2015). Can familiarity lessen the effect of locality? A case study of Mandarin Chinese subjects and the following adverbials. Poznań Studies in Contemporary Linguistics, 51(3), 463–85.CrossRefGoogle Scholar
Xu, C. & Liu, H. (2022). The role of working memory in shaping syntactic dependency structures. In Schwieter, J. & Wen, E. (eds.), The Cambridge handbook of working memory and language. Cambridge: Cambridge University Press. 343–67.Google Scholar
Yan, J. & Liu, H. (2022). Semantic roles or syntactic functions: The effects of annotation scheme on the results of dependency measures. Studia Linguistica, 76(2), 406–28.CrossRefGoogle Scholar
Yngve, V. H. (1960). A model and a hypothesis for language structure. Proceedings of the American Philosophical Society, 104(5), 444–66.Google Scholar
Zipf, G. K. (1949). Human behavior and the Principle of Least Effort: An introduction to human ecology. Cambridge: Addison-Wesley Press.Google Scholar

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