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Automatic analysis of syntactic complexity in second language writing
Abstract
We describe a computational system for automatic analysis of syntactic complexity in second language writing using fourteen different measures that have been explored or proposed in studies of second language development. The system takes a written language sample as input and produces fourteen indices of syntactic complexity of the sample based on these measures. The system is designed with advanced second language proficiency research in mind, and is therefore developed and evaluated using college-level second language writing data from the Written English Corpus of Chinese Learners (Wen et al. 2005). Experimental results show that the system achieves very high reliability on unseen test data from the corpus. We illustrate how the system is used in an example application to investigate whether and to what extent each of these measures significantly differentiate between different proficiency levels
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Cites
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405
The Influence of Planning and Task Type on Second Language Performance
Foster, P., & Skehan, P. (1996) · Studies in Second Language Acquisition · doi:10.1017/s0272263100015047 · Cited by 1,147 · cited 1×
“Syntactic complexity is manifest in second language writing in terms of how varied and sophisticated the production units or grammatical structures are (Foster & Skehan 1996, Ortega 2003, Wolfe-Quintero et al. 1998).”
BackgroundVocabulary and Applied Linguistics
(1992) · doi:10.1007/978-1-349-12396-4 · Cited by 375
Cited by
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Defining and operationalising L2 complexity
Bulté, B., & Housen, A. (2012) · Language Learning & Language Teaching · doi:10.1075/lllt.32.02bul · Cited by 587
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KYLE, K., & CROSSLEY, S. A. (2018) · The Modern Language Journal · doi:10.1111/modl.12468 · Cited by 405
Lu, X., & Ai, H. (2015) · Journal of Second Language Writing · doi:10.1016/j.jslw.2015.06.003 · Cited by 350
Self-citationYang, W., Lu, X., & Weigle, S. C. (2015) · Journal of Second Language Writing · doi:10.1016/j.jslw.2015.02.002 · Cited by 349
Self-citationLink, S., Mehrzad, M., & Rahimi, M. (2022) · Computer Assisted Language Learning · doi:10.1080/09588221.2020.1743323 · Cited by 275
Individual Differences and the Ergodicity Problem
Lowie, W. M., & Verspoor, M. H. (2019) · Language Learning · doi:10.1111/lang.12324 · Cited by 244
Automated classification of primary progressive aphasia subtypes from narrative speech transcripts
Fraser, K. C., Meltzer, J. A., Graham, N. L., Leonard, C., Hirst, G., Black, S. E., & Rochon, E. (2014) · Cortex · doi:10.1016/j.cortex.2012.12.006 · Cited by 229
Syntactic and lexical development in an intensive English for academic purposes programme.
Mazgutova, D., & Kormos, J. (2015) · Journal of Second Language Writing · doi:10.1016/j.jslw.2015.06.004 · Cited by 209
The phraseological dimension in interlanguage complexity research
Paquot, M. (2019) · Second Language Research · doi:10.1177/0267658317694221 · Cited by 175
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Aremu, A., Bosselut, A., Chandu, K. R., Clinciu, M.-A., Das, D., Dhole, K., et al. (2021) · Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021) · doi:10.18653/v1/2021.gem-1.10 · Cited by 159
Jiang, J., Bi, P., & Liu, H. (2019) · Journal of Second Language Writing · doi:10.1016/j.jslw.2019.100666 · Cited by 109
Hwang, W.-Y., Nurtantyana, R., Purba, S. W. D., Hariyanti, U., Indrihapsari, Y., & Surjono, H. D. (2023) · Journal of Educational Computing Research · doi:10.1177/07356331221137253 · Cited by 104
Testing the viability of ChatGPT as a companion in L2 writing accuracy assessment
Mizumoto, A., Shintani, N., Sasaki, M., & Teng, M. F. (2024) · Research Methods in Applied Linguistics · doi:10.1016/j.rmal.2024.100116 · Cited by 104
Task complexity, modality, and working memory in L2 task performance
Cho, M. (2018) · System · doi:10.1016/j.system.2017.10.010 · Cited by 98
Experiments in open domain deception detection
Pérez-Rosas, V., & Mihalcea, R. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1133 · Cited by 97
MEASURING LONGITUDINAL WRITING DEVELOPMENT USING INDICES OF SYNTACTIC COMPLEXITY AND SOPHISTICATION
Kyle, K., Crossley, S., & Verspoor, M. (2021) · Studies in Second Language Acquisition · doi:10.1017/s0272263120000546 · Cited by 83
Dynamic Development in Speaking Versus Writing in Identical Twins
Chan, H., Verspoor, M., & Vahtrick, L. (2015) · Language Learning · doi:10.1111/lang.12107 · Cited by 68
Knoch, U., Rouhshad, A., & Storch, N. (2014) · Assessing Writing · doi:10.1016/j.asw.2014.01.001 · Cited by 66
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Sun, Q., & Zhang, L. J. (2023) · Current Psychology · doi:10.1007/s12144-022-03416-0 · Cited by 40
Rentoumi, V., Paliouras, G., Danasi, E., Arfani, D., Fragkopoulou, K., Varlokosta, S., & Papadatos, S. (2017) · 2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom) · doi:10.1109/coginfocom.2017.8268212 · Cited by 39
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Woo, D. J., Susanto, H., Yeung, C. H., Guo, K., & Fung, A. K. Y. (2023) · arXiv · doi:10.48550/arxiv.2304.02478 · Cited by 39
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Li, W., & Liu, H. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03209-9 · Cited by 36
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Kuiken, F. (2023) · Linguistics Vanguard · doi:10.1515/lingvan-2021-0112 · Cited by 34
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Faroqi-Shah, Y., Treanor, A., Ratner, N. B., Ficek, B., Webster, K., & Tsapkini, K. (2020) · Journal of Communication Disorders · doi:10.1016/j.jcomdis.2020.105994 · Cited by 32
Xue, Y. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12596-0 · Cited by 33
Identifying and Avoiding Confusion in Dialogue with People with Alzheimer's Disease
Chinaei, H., Currie, L. C., Danks, A., Lin, H., Mehta, T., & Rudzicz, F. (2017) · Computational Linguistics · doi:10.1162/coli_a_00290 · Cited by 30
Linguistic complexity as the predictor of EFL independent and integrated writing quality
Zhang, Y., & Ouyang, J. (2023) · Assessing Writing · doi:10.1016/j.asw.2023.100727 · Cited by 28
Bao, T., Zhao, Y., Mao, J., & Zhang, C. (2025) · Scientometrics · doi:10.1007/s11192-025-05341-y · Cited by 25
Talk2Me: Automated linguistic data collection for personal assessment
Komeili, M., Pou-Prom, C., Liaqat, D., Fraser, K. C., Yancheva, M., & Rudzicz, F. (2019) · PLOS ONE · doi:10.1371/journal.pone.0212342 · Cited by 23
Murakami, A. (2014) · Apollo - University of Cambridge Repository · doi:10.17863/cam.16509 · Cited by 20
Agmon, G., Pradhan, S., Ash, S., Nevler, N., Liberman, M., Grossman, M., & Cho, S. (2024) · Journal of Speech, Language, and Hearing Research · doi:10.1044/2023_jslhr-23-00009 · Cited by 17
A comparative study on lexical and syntactic features of ESL versus EFL learners’ writing
Zhang, C., & Kang, S. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1002090 · Cited by 11
Jiang, Z., & Zhang, Z. (2025) · Research Methods in Applied Linguistics · doi:10.1016/j.rmal.2025.100237 · Cited by 10
Wang, Z. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.974481 · Cited by 5
“Therefore, a few syntactic complexity measures were selected for this study based on Lu (2011) to gauge the complexity of syntactic organization by L2 Syntactic Complexity Analyzer (hereafter L2SCA; Lu, 2010) at the sentential, clausal and phrasal levels (refer to Table 1).”
MethodAI chatbots in higher education: Comparing expectations to evidence
Thoeni, A., & Fryer, L. K. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.101061 · Cited by 4
Metadata
- Authors
- Lu, X.
- Venue
- International Journal of Corpus Linguistics
- Publisher
- John Benjamins Publishing Company
- Published
- 2010-10-29
- Volume
- 15
- Issue
- 4
- Pages
- 474-496
- ISSN
- 1384-6655 (ISSN-L)
- 1569-9811
- OpenAlex
- W1971020201
- Cited by
- 1,041
- References
- 3
- FWCI
- 21.03
- Type
- journal article
- Language
- en
- Open access
- closed
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