Classification of speaking proficiency level by machine learning and feature selection

Brendan Flanagan, Sachio Hirokawa, Emiko Kaneko, Emi Izumi

    研究成果: Chapter in Book/Report/Conference proceedingConference contribution

    抄録

    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.

    本文言語英語
    ホスト出版物のタイトルEmerging Technologies for Education - 1st International Symposium, SETE 2016 Held in Conjunction with ICWL 2016, Revised Selected Papers
    編集者Rosella Gennari, Yiwei Cao, Yueh-Min Huang, Wu Wu, Haoran Xie
    出版社Springer Verlag
    ページ677-682
    ページ数6
    ISBN(印刷版)9783319528359
    DOI
    出版ステータス出版済み - 2017
    イベント1st International Symposium on Emerging Technologies for Education, SETE 2016 Held in Conjunction with ICWL 2016 - Rome, イタリア
    継続期間: 10 26 201610 29 2016

    出版物シリーズ

    名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    10108 LNCS
    ISSN(印刷版)0302-9743
    ISSN(電子版)1611-3349

    その他

    その他1st International Symposium on Emerging Technologies for Education, SETE 2016 Held in Conjunction with ICWL 2016
    国/地域イタリア
    CityRome
    Period10/26/1610/29/16

    All Science Journal Classification (ASJC) codes

    • 理論的コンピュータサイエンス
    • コンピュータ サイエンス(全般)

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