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Prediction of Learner Native Language by Writing Error Pattern

  • Brendan FlanaganEmail author
  • Chengjiu Yin
  • Takahiko Suzuki
  • Sachio Hirokawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9192)

Abstract

The native language of a foreign language learner can have an effect on the errors they make because of similarities or differences between the two languages. In order to provide effective error prediction and correction for non-native English language learners it is important to identify their specific characteristic error patterns that are influenced by their native language. In this paper, we examine analyzing error detection scores to predict the native language of an English language learner. 15 categories of error detection scores are combined to create an error prediction score vector representation of each sentence. The native language is predicted by training an SVM classifier with the error vectors. The results are compared to an SVM classifier trained with just word representations of the learner writing sentences.

Keywords

Native language prediction Writing errors SVM classifier 

Notes

Acknowledgment

This work was partially supported by JSPS KAKENHI Grant Number 24500176.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Brendan Flanagan
    • 1
    Email author
  • Chengjiu Yin
    • 2
  • Takahiko Suzuki
    • 3
  • Sachio Hirokawa
    • 3
  1. 1.Graduate School of Information Science and Electrical EngineeringKyushu UniversityFukuokaJapan
  2. 2.Faculty of Arts and ScienceKyushu UniversityFukuokaJapan
  3. 3.Research Institute for Information TechnologyKyushu UniversityFukuokaJapan

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