Volume 7, Issue 1
  • ISSN 2215-1478
  • E-ISSN: 2215-1486
Preview this article:

This work was made publicly available by the publisher.

Article metrics loading...

Loading full text...

Full text loading...



  1. Alexopoulou, T. , Michel, M. , Murakami, A. , & Meurers, D.
    (2017) Task Effects on Linguistic Complexity and Accuracy: A Large-Scale Learner Corpus Analysis Employing Natural Language Processing Techniques. Language Learning, 67(S1), 180–208. 10.1111/lang.12232
    https://doi.org/10.1111/lang.12232 [Google Scholar]
  2. Anthony, L.
    (2014) AntWordProfiler (Version 1.4. 1)[Computer Software]. Tokyo, Japan: Waseda University.
    [Google Scholar]
  3. (2019) AntConc (3.5.8) [Computer software]. Tokyo, Japan: Waseda University.
    [Google Scholar]
  4. Bauer, L. , & Nation, I. S. P.
    (1993) Word families. International Journal of Lexicography, 6(4), 253–279. 10.1093/ijl/6.4.253
    https://doi.org/10.1093/ijl/6.4.253 [Google Scholar]
  5. Berzak, Y. , Kenney, J. , Spadine, C. , Wang, J. X. , Lam, L. , Mori, K. S. , Garza, S. , & Katz, B.
    (2016) Universal dependencies for learner English. InProceedings of the 54th Annual Meeting of the Association for Computational Linguistics (pp.737–746). Stroudsburg: Association for Computational Linguistics.
    [Google Scholar]
  6. Bestgen, Y. , & Granger, S.
    (2014) Quantifying the development of phraseological competence in L2 English writing: An automated approach. Journal of Second Language Writing, 26, 28–41. doi:  10.1016/j.jslw.2014.09.004
    https://doi.org/10.1016/j.jslw.2014.09.004 [Google Scholar]
  7. Biber, D.
    (1988) Variation across speech and writing. Cambridge: Cambridge University Press. 10.1017/CBO9780511621024
    https://doi.org/10.1017/CBO9780511621024 [Google Scholar]
  8. Biber, D. , Gray, B. , & Staples, S.
    (2014) Predicting Patterns of Grammatical Complexity Across Language Exam Task Types and Proficiency Levels. Applied Linguistics, 37(5), 639–668. doi:  10.1093/applin/amu059
    https://doi.org/10.1093/applin/amu059 [Google Scholar]
  9. Chen, D. , & Manning, C. D.
    (2014) A fast and accurate dependency parser using neural networks. InProceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp.740–750). Stroudsburg: Association for Computational Linguistics. 10.3115/v1/D14‑1082
    https://doi.org/10.3115/v1/D14-1082 [Google Scholar]
  10. Choi, J. D. , Tetreault, J. , & Stent, A.
    (2015) It depends: Dependency parser comparison using a web-based evaluation tool. InProceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (pp.387–396). Stroudsburg: Association for Computational Linguistics.
    [Google Scholar]
  11. Cobb, T.
    (2018) Web VocabProfile (WebVP). [Computer Software].
    [Google Scholar]
  12. Crossley, S. A. , Kyle, K. , & Dascalu, M.
    (2019) The Tool for the Automatic Analysis of Cohesion 2.0: Integrating semantic similarity and text overlap. Behavior Research Methods, 51(1), 14–27. doi:  10.3758/s13428‑018‑1142‑4
    https://doi.org/10.3758/s13428-018-1142-4 [Google Scholar]
  13. Crossley, S. A. , & McNamara, D. S.
    (2012) Predicting second language writing proficiency: The roles of cohesion and linguistic sophistication. Journal of Research in Reading, 35(2), 115–135. 10.1111/j.1467‑9817.2010.01449.x
    https://doi.org/10.1111/j.1467-9817.2010.01449.x [Google Scholar]
  14. Díez-Bedmar, M. B. , & Pérez-Paredes, P.
    (2020) Noun phrase complexity in young Spanish EFL learners’ writing: Complementing syntactic complexity indices with corpus-driven analyses. International Journal of Corpus Linguistics, 25(1), 4–35. 10.1075/ijcl.17058.die
    https://doi.org/10.1075/ijcl.17058.die [Google Scholar]
  15. Explosion AI
    Explosion AI (2018) spaCy language models. Retrieved fromhttps://spacy.io/models/en#en_core_web_sm
    [Google Scholar]
  16. Garside, R. , Leech, G. N. , & McEnery, T.
    (1997) Corpus annotation: Linguistic information from computer text corpora. Harlow: Longman. 10.4324/9781315841366
    https://doi.org/10.4324/9781315841366 [Google Scholar]
  17. Geertzen, J. , Alexopoulou, T. , & Korhonen, A.
    (2013) Automatic linguistic annotation of large scale L2 databases: The EF-Cambridge Open Language Database (EFCAMDAT). In R. T. Miller , K. I. Martin , C. M. Eddington , A. Henery , N. Marcos Miguel , A. M. Tseng , A. Tuninetti , & D. Walter (Eds.), Selected Proceedings of the 2012 Second Language Research Forum (pp.240–254). Somerville, MA: Cascadilla Proceedings Project.
    [Google Scholar]
  18. Graesser, A. C. , McNamara, D. S. , Louwerse, M. M. , & Cai, Z.
    (2004) Coh-Metrix: Analysis of text on cohesion and language. Behavior Research Methods, Instruments, & Computers, 36(2), 193–202. doi:  10.3758/BF03195564
    https://doi.org/10.3758/BF03195564 [Google Scholar]
  19. Granger, S. , & Bestgen, Y.
    (2017) Using collgrams to assess L2 phraseological development: A replication study. In P. Haan , R. de Vries , & S. van Vuuren (Eds.), Language, Learners and Levels: Progression and Variation (pp.385–408). Louvain-la-Neuve: Presses universitaires de Louvain.
    [Google Scholar]
  20. Green, C.
    (2019) Enriching the academic wordlist and Secondary Vocabulary Lists with lexicogrammar: Toward a pattern grammar of academic vocabulary. System, 87, 102158. doi:  10.1016/j.system.2019.102158
    https://doi.org/10.1016/j.system.2019.102158 [Google Scholar]
  21. Heatley, A. , & Nation, I. S. P.
    (1994) Range. [Computer Software]. Victoria University of Wellington, NZ. Retrieved fromWww.Vuw.Ac.Nz/Lals/
    [Google Scholar]
  22. Huang, Y. , Murakami, A. , Alexopoulou, T. , & Korhonen, A.
    (2018) Dependency parsing of learner English. International Journal of Corpus Linguistics, 23(1), 28–54. 10.1075/ijcl.16080.hua
    https://doi.org/10.1075/ijcl.16080.hua [Google Scholar]
  23. Jurafsky, D. , & Manning, C. D.
    (2008) Speech and language processing: An introduction to natural language processing, speech recognition, and computational linguistics (2nd ed.). Upper Saddle River: Prentice-Hall.
    [Google Scholar]
  24. Jurafsky, D. , & Martin, J. H.
    (2019) Speech and Language Processing (Unpublished Manuscript). October 2019 Retrieved fromhttps://web.stanford.edu/~jurafsky/slp3/
    [Google Scholar]
  25. Khushik, G. A. , & Huhta, A.
    (2020) Investigating Syntactic Complexity in EFL Learners’ Writing across Common European Framework of Reference Levels A1, A2, and B1. Applied Linguistics, 41(4), 506–532. doi:  10.1093/applin/amy064
    https://doi.org/10.1093/applin/amy064 [Google Scholar]
  26. Kitaev, N. , & Klein, D.
    (2018) Constituency parsing with a self-attentive encoder. InProceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp.2676–2686). Stroudsburg: Association for Computational Linguistics. 10.18653/v1/P18‑1249
    https://doi.org/10.18653/v1/P18-1249 [Google Scholar]
  27. Klein, D. , & Manning, C. D.
    (2003) Accurate unlexicalized parsing. InProceedings of the 41st Annual Meeting of the Association for Computational Linguistics (pp.423–430). Stroudsburg: Association for Computational Linguistics. doi:  10.3115/1075096.1075150
    https://doi.org/10.3115/1075096.1075150 [Google Scholar]
  28. Kyle, K.
    (2016) Measuring Syntactic Development in L2 Writing: Fine Grained Indices of Syntactic Complexity and Usage-Based Indices of Syntactic Sophistication (Unpublished doctorial dissertation). Georgia State University, Atlanta. scholarworks.gsu.edu/alesl_diss/35/
  29. Kyle, K. , & Crossley, S. A.
    (2017) Assessing syntactic sophistication in L2 writing: A usage-based approach. Language Testing, 34(4), 513–535. 10.1177/0265532217712554
    https://doi.org/10.1177/0265532217712554 [Google Scholar]
  30. (2018) Measuring Syntactic Complexity in L2 Writing Using Fine-Grained Clausal and Phrasal Indices. The Modern Language Journal, 102(2), 333–349. doi:  10.1111/modl.12468
    https://doi.org/10.1111/modl.12468 [Google Scholar]
  31. Kyle, K. , Crossley, S. A. , & Verspoor, M.
    (in press). Measuring longitudinal writing development using indices of syntactic complexity and VAC sophistication. Studies in Second Language Acquisition.
    [Google Scholar]
  32. Kyle, K. , & Eguchi, M.
    (in press). Automatically assessing lexical sophistication using word, bigram, and dependency indices. In S. Granger Ed. Perspectives on the Second Language Phrasicon: The View from Learner Corpora. Bristol: Multilingual Matters.
    [Google Scholar]
  33. (in progress). A gold standard part of speech tagged and dependency parsed corpus of L2 speech.
    [Google Scholar]
  34. Levy, R. , & Andrew, G.
    (2006) Tregex and Tsurgeon: Tools for querying and manipulating tree data structures. InProceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06) (pp.2231–2234). European Language Resources Association (ELRA).
    [Google Scholar]
  35. Lu, X.
    (2010) Automatic analysis of syntactic complexity in second language writing. International Journal of Corpus Linguistics, 15(4), 474–496. doi:  10.1075/ijcl.15.4.02lu
    https://doi.org/10.1075/ijcl.15.4.02lu [Google Scholar]
  36. Lu, X. , & Ai, H.
    (2015) Syntactic complexity in college-level English writing: Differences among writers with diverse L1 backgrounds. Journal of Second Language Writing, 29, 16–27. 10.1016/j.jslw.2015.06.003
    https://doi.org/10.1016/j.jslw.2015.06.003 [Google Scholar]
  37. McNamara, D. S. , Graesser, A. C. , McCarthy, P. M. , & Cai, Z.
    (2014) Automated evaluation of text and discourse with Coh-Metrix. Cambridge: Cambridge University Press. 10.1017/CBO9780511894664
    https://doi.org/10.1017/CBO9780511894664 [Google Scholar]
  38. Meurers, D. , & Dickinson, M.
    (2017) Evidence and interpretation in language learning research: Opportunities for collaboration with computational linguistics. Language Learning, 67(S1), 66–95. 10.1111/lang.12233
    https://doi.org/10.1111/lang.12233 [Google Scholar]
  39. Nivre, J. , Hall, J. , & Nilsson, J.
    (2006) MaltParser: A Data-Driven Parser-Generator for Dependency Parsing. InProceedings of the fifth international conference on language resources and evaluation (LREC’06) (pp.2216–2219). European Language Resources Association (ELRA).
    [Google Scholar]
  40. Paquot, M.
    (2018) Phraseological Competence: A Missing Component in University Entrance Language Tests? Insights From a Study of EFL Learners’ Use of Statistical Collocations. Language Assessment Quarterly, 15(1), 29–43. 10.1080/15434303.2017.1405421
    https://doi.org/10.1080/15434303.2017.1405421 [Google Scholar]
  41. (2019) The phraseological dimension in interlanguage complexity research. Second Language Research, 35(1), 121–145. 10.1177/0267658317694221
    https://doi.org/10.1177/0267658317694221 [Google Scholar]
  42. Paquot, M. , Naets, H. , & Gries, S. T.
    (in press). Using syntactic co-occurrences to trace phraseological complexity development in learner writing: Verb + object structures in LONGDALE. In B. LeBruyn & M. Paquot Eds. Learner Corpus Research Meets Second Language Acquisition. Cambridge: Cambridge University Press.
    [Google Scholar]
  43. Pinchbeck, G. G.
    (2017) Vocabulary Use in Academic-Track High-School English Literature Diploma Exam Essay Writing and its Relationship to Academic Achievement (Unpublished doctoral dissertation). University of Calgary, Calgary.
    [Google Scholar]
  44. Polio, C. , & Yoon, H.
    (2018) The reliability and validity of automated tools for examining variation in syntactic complexity across genres. International Journal of Applied Linguistics, 28(1), 165–188. 10.1111/ijal.12200
    https://doi.org/10.1111/ijal.12200 [Google Scholar]
  45. Schmid, H.
    (1994) Probabilistic part-of-speech tagging using decision trees. InInternational Conference on New Methods in Language Processing (pp.44–49). Manchester, UK.
    [Google Scholar]
  46. (1995) Treetagger: A language independent part-of-speech tagger [Computer software]Institut Für Maschinelle Sprachverarbeitung, Universität Stuttgart, Stuttgart.
    [Google Scholar]
  47. Scott, M.
    (2020) WordSmith Tools (8.0) [Computer software]. Liverpool: Lexical Analysis Software.
    [Google Scholar]
  48. Toutanova, K. , Klein, D. , Manning, C. D. , & Singer, Y.
    (2003) Feature-rich part-of-speech tagging with a cyclic dependency network. InProceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology – Volume 1 (pp.173–180). Stroudsburg: Association for Computational Linguistics.
    [Google Scholar]
  49. van den Bosch, A. , Busser, B. , Canisius, S. , & Daelemans, W.
    (2007) An efficient memory-based morphosyntactic tagger and parser for Dutch. In P. Dirix , I. Schuurman , V. Vandeghinste , & F. Van Eynde (Eds.), Proceedings of the 17th meeting of Computational Linguistics in the Netherlands (pp.191–206).
    [Google Scholar]
  50. van Noord, G.
    (2006) At last parsing is now operational. InTALN 2006 (pp.20–42).
    [Google Scholar]
  51. Weischedel, R. , Palmer, M. , Marcus, M. , Hovy, E. , Pradhan, S. , Ramshaw, L. , Xue, N. , Taylor, A. , Kaufman, J. , & Franchini, M.
    (2013) Ontonotes release 5.0. Philadelphia: Linguistic Data Consortium. Retrieved fromhttps://catalog.ldc.upenn.edu/LDC2013T19
    [Google Scholar]
  52. Yannakoudakis, H. , Briscoe, T. , & Medlock, B.
    (2011) A new dataset and method for automatically grading ESOL texts. InProceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (pp.180–189). Stroudsburg: Association for Computational Linguistics.
    [Google Scholar]
  • Article Type: Introduction
This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error