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Title: A Multi-model SVR Approach to Estimating the CEFR Proficiency Level of Grammar Item Features
Authors: Flanagan, Brendan
Hirokawa, Sachio
Kaneko, Emiko
Izumi, Emi
Ogata, Hiroaki  kyouindb  KAKEN_id
Author's alias: 廣川, 佐千男
緒方, 広明
Issue Date: Jul-2017
Publisher: IEEE
Journal title: 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)
Start page: 521
End page: 526
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.
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。
DOI(Published Version): 10.1109/IIAI-AAI.2017.169
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