英语母语者不同学术文体的计量研究
A Quantitative Study of Different Academic Genres of Native English Speakers
DOI: 10.12677/ML.2024.122115, PDF,    科研立项经费支持
作者: 周婉婷：浙江财经大学外国语学院，浙江 杭州
关键词: 定量研究；依存距离；句长；文体；句法复杂度；Quantitative Research； Dependency Distance； Sentence Length； Genre； Syntactic Complexity
摘要: 文体的句法复杂性一直受到学者们的关注，依存距离是评估句法复杂性的指标之一。以往的研究发现，依存距离、句子长度和文体三者密切相关。然而，很少有研究系统地考察英语母语者不同学术文体下，依存距离和句子长度的分布情况。本研究基于LONCESS语料库，采用定量方法研究了议论文和散文的句长分布和依存距离分布，以及两者之间的关系。结果表明：1) 两种文体的句长分布遵循不同的分布模型。2) 两种文体的依存距离分布受体裁的影响有一定的规律性。3) 关于句长和平均依存距离之间的关系，线性回归模型能较好地拟合议论文体，复合曲线模型能较好地拟合散文文体。总之，研究结果表明，两种文体的句子长度和依存距离的概率分布遵循长尾效应，表明人类的工作记忆是有限的。
Abstract: The syntactic complexity of genres has always been of interest to scholars, and dependency distance is one of the indicators for assessing syntactic complexity. Previous studies have found that de-pendency distance, sentence length and genre are closely related. However, few studies have sys-tematically examined the distribution of dependency distance and sentence length in different genres of native English speakers. In this study, based on the LONCESS corpus, a quantitative method was used to investigate the distribution of sentence length and the distribution of depend-ency distance in argumentative and prose genres, as well as the relationship between the two. The results show that: 1) The sentence length distributions of the two genres follow different distribu-tion models. 2) The dependency distance distributions of the two genres are affected by the genres with a certain regularity. 3) Regarding the relationship between the sentence length and the aver-age dependency distance, the linear regression model fits the argumentative genre better, and the composite curve model fits the literary genre better. In conclusion, the results show that the proba-bility distributions of sentence length and dependency distance for both genres follow the long-tail effect, indicating that human working memory is limited.
文章引用：周婉婷. 英语母语者不同学术文体的计量研究[J]. 现代语言学, 2024, 12(2): 843-854. https://doi.org/10.12677/ML.2024.122115

参考文献

[1] Brunner, J.S. (1986) Actual Minds, Possible Worlds. Harvard University Press, Cambridge. [Google Scholar] [CrossRef] 
[2] Biber, D. and Conrad, S. (2009) Register, Genre, and Style. Cam-bridge University Press, Cambridge. [Google Scholar] [CrossRef] 
[3] Scott, C.M. and Windsor, J. (2000) General Language Perfor-mance Measures in Spoken and Written Narrative and Expository Discourse of School-Age Children with Language Learning Disabilities. Journal of Speech, Language, and Hearing Research, 43, 324-339. [Google Scholar] [CrossRef] [PubMed]
[4] Casal, J.E., Lu, X., Qiu, X., Wang, Y. and Zhang, G. (2021) Syntactic Complexity across Academic Research Article Part-Genres: A Cross-Disciplinary Perspective. Journal of English for Academic Purposes, 52, Article ID: 100996. [Google Scholar] [CrossRef] 
[5] Lu, X., Casal, J.E., Liu, Y., Kisselev, O. and Yoon, J. (2021) The Relationship between Syntactic Complexity and Rhetorical Move-Steps in Research Article Introductions: Variation among Four Social Science and Engineering Disciplines. Journal of English for Academic Purposes, 52, Article ID: 101006. [Google Scholar] [CrossRef] 
[6] Yu, S. (2021) Giving Genre-Based Peer Feedback in Academic Writing: Sources of Knowledge and Skills, Difficulties and Challenges. Assessment & Evaluation in Higher Education, 46, 36-53. [Google Scholar] [CrossRef] 
[7] Jeong, H. (2017) Narrative and Expository Genre Effects on Students, Raters, and Performance Criteria. Assessing Writing, 31, 113-125. [Google Scholar] [CrossRef] 
[8] Pu, L., Heng, R. and Cao, C. (2022) The Effects of Genre on the Syntactic Complexity of Argumentative and Expository Writing by Chinese EFL Learners. Frontiers in Psychology, 13, Article 1047117. [Google Scholar] [CrossRef] [PubMed]
[9] Wang, Y. and Liu, H. (2017) The Effects of Genre on Depend-ency Distance and Dependency Direction. Language Sciences, 59, 135-147. [Google Scholar] [CrossRef] 
[10] Liu, H.T. (2008) Dependency Distance as a Metric of Language Comprehension Difficulty. Journal of Cognitive Science, 9, 159-191. [Google Scholar] [CrossRef] 
[11] Jiang, J. and Ouyang, J. (2017) Dependency Distance: A New Per-spective on the Syntactic Development in Second Language Acquisition: Comment on “Dependency Distance: A New Perspective on Syntactic Patterns in Natural Language” by Haitao Liu et al. Physics of Life Reviews, 21, 209-210. [Google Scholar] [CrossRef] [PubMed]
[12] Ouyang, J., Jiang, J. and Liu, H. (2022) Dependency Distance Measures in Assessing L2 Writing Proficiency. Assessing Writing, 51, Article ID: 100603. [Google Scholar] [CrossRef] 
[13] Oya, M. (2013) Degree Centralities, Closeness Centralities, and Dependency Distances of Different Genres of Texts. Proceedings of the 17th Conference of Pan-Pacific Association of Applied Linguistics, Beijing, 21-23 August 2012, 89-90.
[14] Jiang, J. and 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. [Google Scholar] [CrossRef] 
[15] Oya, M. (2011) Syntactic Dependency Distance as Sentence Complexity Measure. Proceedings of the 16th International Conference of Pan-Pacific Association of Applied Linguistics, Hongkong, 8-10 August 2011, 313-316.
[16] Hiranuma, S. (1999) Syntactic Difficulty in English and Japanese: A tex-tual study. UCL Working Papers in Linguistics, 11, 309-322.
[17] Chen, R., Deng, S. and Liu, H. (2021) Syntactic Complexity of Different Text Types: From the Perspective of Dependency Distance Both Linearly and Hierarchically. Journal of Quantitative Linguistics, 29, 510-540. [Google Scholar] [CrossRef] 
[18] Gao, N. and He, Q. (2023) A Corpus-Based Study of the Dependency Distance Differences in English Academic Writing. SAGE Open, 13, 1-12. [Google Scholar] [CrossRef] 
[19] Liu, H. (2009) Dependency Grammar: From Theory to Practice. Science Press, Beijing.
[20] Mačutek, J. and Wimmer, G. (2013) Evaluating Goodness-of-Fit of Discrete Distribution Models in Quantitative Linguistics. Journal of Quantitative Linguistics, 20, 227-240. [Google Scholar] [CrossRef] 
[21] Liu, H., Xu, C. and Liang, J. (2017) Dependency Distance: A New Perspective on Syntactic Patterns in Natural Languages. Physics of Life Reviews, 21, 171-193. [Google Scholar] [CrossRef] [PubMed]
[22] Baayen, R.H. (2001) Word Frequency Distributions. Springer, Dordrecht. [Google Scholar] [CrossRef] 
[23] Köhler, R. and Altmann, G. (2000) Probability Distri-butions of Syntactic Units and Properties. Journal of Quantitative Linguistics, 7, 189-200. [Google Scholar] [CrossRef] 
[24] Popescu, I.I., Best, K.H. and Altmann, G. (2014) Unified Modeling of Length in Language. RAM-Verlag, Lüdenscheid.
[25] Covington, M. (2003) A Free-Word-Order Dependency Parser in Prolog. The University of Georgia, Athens.
[26] Cowan, N. (2001) The Magical Number 4 in Short-Term Memory: A Reconsideration of Mental Storage Capacity. Behavioral and Brain Sciences, 24, 87-185. [Google Scholar] [CrossRef] 
[27] Grabe, W. (2002) Narrative and Expository Macro-Genres. In: Johns, A.M., Ed., Genre in the Classroom: Multiple Perspectives, Lawrence Erlbaum Associate, Mahwah, 249-267.
[28] Ferrer-i-Cancho, R. and Arias, M. (2013) Non-Linear Regression on Dependency Trees. Lecture on Complex and Social Networks.
[29] Bain, A. (1967) English Composition and Rhetoric. 2nd Edition, Appleton & Company, New York.
[30] Genung, J.F. (1900) The Working Principles of Rhetoric: Examined in Their Literary Rela-tions and Illustrated with Examples. Ginn & Company, Boston.
[31] Weigle, S.C. (2002) Assessing Writing. Cambridge University Press, Cambridge. [Google Scholar] [CrossRef]