Abstract
Analysis of publicly available language learning corpora can be useful for extracting characteristic features of learners from different proficiency levels. This can then be used to support language learning research and the creation of educational resources. In this paper, we classify the words and parts of speech of transcripts from different speaking proficiency levels found in the NICT-JLE corpus. The characteristic features of learners who have the equivalent spoken proficiency of CEFR levels A1 through to B2 were extracted by analyzing the data with the support vector machine method. In particular, we apply feature selection to find a set of characteristic features that achieve optimal classification performance, which can be used to predict spoken learner proficiency.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Izumi, E., Uchimoto, K., Isahara, H.: The NICT JLE Corpus. ACL Publishing (2004). (in Japanese)
Izumi, E., Uchimoto, K., Isahara, H.: The NICT JLE corpus: exploiting the language learner’s speech database for research and education. Int. J. Comput. Internet Manag. 12(2), 119–125 (2004)
Izumi, E., Uchimoto, K., Isahara, H.: The overview of the SST speech corpus of Japanese learner English and evaluation through the experiment on automatic detection of learners’ errors. In: 4th International Conference on Language Resources and Evaluation, pp. 1435–1438 (2004)
Council of Europe: Common European Framework of Reference for Languages: Learning, Teaching, Assessment. Cambridge University Press, Cambridge (2001)
Tono, Y. (ed.): The CEFR-J Handbook: A Resource Book for Using CAN-DO Descriptors for English Language Teaching. Taishukan Publishing (2013). (in Japanese)
Page, E.B.: The use of the computer in analyzing student essays. Int. Rev. Educ. 14(2), 210–225 (1968)
Supnithi, T., Uchimoto, K., Saiga, T., Izumi, E., Virach, S., Isahara, H.: Automatic proficiency level checking based on SST corpus. In: Proceedings of the RANLP, pp. 29–33 (2003)
Abe, M.: Frequency change patterns across proficiency levels in Japanese EFL learner speech. Apples: J. Appl. Lang. Stud. 8(3), 85–96 (2014)
Flanagan, B., Hirokawa, S.: The relationship of English foreign language learner proficiency and an entropy based measure. IEE 1(3), 29–38 (2015)
Joachims, T.: Training linear SVMs in linear time. In: Proceedings of the ACM-KDD, pp. 217–226 (2006)
Sakai, T., Hirokawa, S.: Feature words that classify problem sentence in scientific article. In: Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services, pp. 360–367 (2012)
Acknowledgment
This work was supported by JSPS KAKENHI Grant Number 15H02778, 24242017, and 15J04830.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Flanagan, B., Hirokawa, S., Kaneko, E., Izumi, E. (2017). Classification of Speaking Proficiency Level by Machine Learning and Feature Selection. In: Wu, TT., Gennari, R., Huang, YM., Xie, H., Cao, Y. (eds) Emerging Technologies for Education. SETE 2016. Lecture Notes in Computer Science(), vol 10108. Springer, Cham. https://doi.org/10.1007/978-3-319-52836-6_72
Download citation
DOI: https://doi.org/10.1007/978-3-319-52836-6_72
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-52835-9
Online ISBN: 978-3-319-52836-6
eBook Packages: Computer ScienceComputer Science (R0)