oa Profiling English sentences based on CEFR levels

References

1. Alfter, D., Tiedemann, T. L., & Volodina, E.
   (2020) Expert judgments versus crowdsourcing in ordering multi-word expressions. Eighth Swedish Language Technology Conference (SLTC). https://gubox.app.box.com/v/SLTC-2020-paper-16
   [Google Scholar]
2. Arase, Y., Uchida, S., & Kajiwara, T.
   (2022) CEFR-based sentence difficulty annotation and assessment. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 6206–6219. 10.18653/v1/2022.emnlp‑main.416
   https://doi.org/10.18653/v1/2022.emnlp-main.416 [Google Scholar]
3. Bax, S.
   (2012) Text inspector: Online text analysis tool. https://textinspector.com/
   [Google Scholar]
4. Capel, A.
   (2015) The English vocabulary profile. InJ. Harrison & F. Barker (Eds.), English profile in practice51 (pp.9–27). Cambridge University Press.
   [Google Scholar]
5. Chen, Y. H., & Baker, P.
   (2016) Investigating criterial discourse features across second language development: Lexical bundles in rated learner essays, CEFR B1, B2 and C1. Applied Linguistics, 37(6), 849–880. 10.1093/applin/amu065
   https://doi.org/10.1093/applin/amu065 [Google Scholar]
6. Chujo, K., Oghigian, K., & Akasegawa, S.
   (2015) A corpus and grammatical browsing system for remedial EFL learners. InA. Leńko-Szymańska & A. Boulton (Eds.), Multiple affordances of language corpora for data-driven learning (pp.109–130). John Benjamins. 10.1075/scl.69.06chu
   https://doi.org/10.1075/scl.69.06chu [Google Scholar]
7. Collins-Thompson, K.
   (2014) Computational assessment of text readability: A survey of current and future research. ITL-International Journal of Applied Linguistics, 165(2), 97–135. 10.1075/itl.165.2.01col
   https://doi.org/10.1075/itl.165.2.01col [Google Scholar]
8. Douglas, C. E., & Fligner, A. M.
   (1991) On distribution-free multiple comparisons in the one-way analysis of variance. Communications in Statistics: Theory and Methods, 201, 127–139. 10.1080/03610929108830487
   https://doi.org/10.1080/03610929108830487 [Google Scholar]
9. Dürlich, L., & François, T.
   (2018) EFLLex: A graded lexical resource for learners of English as a foreign language. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), 873–879.
   [Google Scholar]
10. Dwass, M.
    (1960) Some k-sample rank-order tests. InI. Olkin, S. G. Ghurye, W. Hoeffding, W. G. Madow, & B. H. Mann (Eds.), Contributions to Probability and Statistics. Essays in Honor of H. Hotelling (pp.198–202). Stanford University Press.
    [Google Scholar]
11. Ehara, Y.
    (2018) Building an English vocabulary knowledge dataset of Japanese English-as-a-second-language learners using crowdsourcing. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), 485–488.
    [Google Scholar]
12. Flesch, R.
    (1948) A new readability yardstick. Journal of Applied Psychology, 32(3), 221–233. 10.1037/h0057532
    https://doi.org/10.1037/h0057532 [Google Scholar]
13. François, T.
    (2015) When readability meets computational linguistics: A new paradigm in readability. Revue française de linguistique appliquée, 20(2), 79–97. hdl.handle.net/2078.1/193855. 10.3917/rfla.202.0079
    https://doi.org/10.3917/rfla.202.0079 [Google Scholar]
14. François, T., & Fairon, C.
    (2012) An “AI readability” formula for French as a foreign language. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 466–477.
    [Google Scholar]
15. Harrison, J.
    (2015) The English grammar profile. InJ. Harrison & F. Barker (Eds.), English profile in practice51 (pp.28–48). Cambridge University Press.
    [Google Scholar]
16. Hawkins, J. A., & Filipović, L.
    (2012) Criterial features in L2 English: Specifying the reference levels of the Common European Framework (Vol.11). Cambridge University Press.
    [Google Scholar]
17. Ishii, Y., & Tono, Y.
    (2018) Investigating Japanese EFL learners’ overuse/underuse of English grammar categories and their relevance to CEFR levels. Proceedings of the 4th Asia Pacific Corpus Linguistics Conference, 160–165.
    [Google Scholar]
18. Jiang, C., Maddela, M., Lan, W., Zhong, Y., & Xu, W.
    (2020) Neural CRF model for sentence alignment in text simplification. arXiv preprintarXiv:2005.02324. 10.18653/v1/2020.acl‑main.709
    https://doi.org/10.18653/v1/2020.acl-main.709 [Google Scholar]
19. Khallaf, N., & Sharoff, S.
    (2021) Automatic difficulty classification of Arabic sentences. Proceedings of the Sixth Arabic Natural Language Processing Workshop, 105–114.
    [Google Scholar]
20. Kilgarriff, A., Husák, M., McAdam, K., Rundell, M., & Rychlý, P.
    (2008) GDEX: Automatically finding good dictionary examples in a corpus. Proceedings of the XIII EURALEX international congress, 11, 425–432.
    [Google Scholar]
21. Kim, S.
    (2021) Generalizability of CEFR criterial grammatical features in a Korean EFL corpus across A1, A2, B1, and B2 levels. Language Assessment Quarterly, 18(3), 273–295. 10.1080/15434303.2020.1855647
    https://doi.org/10.1080/15434303.2020.1855647 [Google Scholar]
22. Klare, G. R.
    (1968) The role of word frequency in readability. Elementary English, 45(1), 12–22.
    [Google Scholar]
23. Marcus, M. P., Santorini, B., & Marcinkiewicz, M. A.
    (1993) Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics, 19(2), 313–330.
    [Google Scholar]
24. Nation, I.
    (2006) How large a vocabulary is needed for reading and listening?Canadian Modern Language Review, 63(1), 59–82. 10.3138/cmlr.63.1.59
    https://doi.org/10.3138/cmlr.63.1.59 [Google Scholar]
25. Nishihara, D., Kajiwara, T., & Arase, Y.
    (2019) Controllable text simplification with lexical constraint loss. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, 260–266. 10.18653/v1/P19‑2036
    https://doi.org/10.18653/v1/P19-2036 [Google Scholar]
26. Ozasa, T., Weir, G., & Fukui, M.
    (2007) Measuring readability for Japanese learners of English. Proceedings of the 12th Conference of Pan-Pacific Association of Applied Linguistics, 122–125.
    [Google Scholar]
27. Pilán, I., Vajjala, S., & Volodina, E.
    (2016) A readable read: Automatic assessment of language learning materials based on linguistic complexity. arXiv preprintarXiv:1603.08868.
    [Google Scholar]
28. Pilán, I., Volodina, E., & Johansson, R.
    (2014) Rule-based and machine learning approaches for second language sentence-level readability. Proceedings of the ninth workshop on innovative use of NLP for building educational applications, 174–184. 10.3115/v1/W14‑1821
    https://doi.org/10.3115/v1/W14-1821 [Google Scholar]
29. Qi, P., Zhang, Y., Zhang, Y., Bolton, J., & Manning, C. D.
    (2020) Stanza: A Python natural language processing toolkit for many human languages. arXiv preprintarXiv:2003.07082. 10.18653/v1/2020.acl‑demos.14
    https://doi.org/10.18653/v1/2020.acl-demos.14 [Google Scholar]
30. Salamoura, A., & Saville, N.
    (2010) Exemplifying the CEFR: Criterial features of written learner English from the English Profile Programme. InI. Bartning, M. Maisa, & I. Vedder (Eds.), Communicative proficiency and linguistic development: Intersections between SLA and language testing research (Vol.11, pp.101–132). European Second Language Association.
    [Google Scholar]
31. Steel, R. G. D.
    (1960) A rank sum test for comparing all pairs of treatments, Technometrics, 21, 197–207. 10.1080/00401706.1960.10489894
    https://doi.org/10.1080/00401706.1960.10489894 [Google Scholar]
32. Uchida, S., & Negishi, M.
    (2018) Assigning CEFR-J levels to English texts based on textual features. Proceedings of the 4th Asia Pacific Corpus Linguistics Conference, 463–467.
    [Google Scholar]
33. Vajjala, S., & Meurers, D.
    (2014) Assessing the relative reading level of sentence pairs for text simplification. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, 288–297. 10.3115/v1/E14‑1031
    https://doi.org/10.3115/v1/E14-1031 [Google Scholar]