Prediction of learner native language by writing error pattern

Brendan Flanagan, Chengjiu Yin, Takahiko Suzuki, Sachio Hirokawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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 nonnative 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.

Original languageEnglish
Title of host publicationLearning and Collaboration Technologies - 2nd International Conference, LCT 2015 Held as Part of HCI International 2015, Proceedings
EditorsPanayiotis Zaphiris, Andri Ioannou
PublisherSpringer Verlag
Pages87-96
Number of pages10
ISBN (Print)9783319206080
DOIs
Publication statusPublished - Jan 1 2015
Event2nd International Conference on Learning and Collaboration Technologies, LCT 2015 Held as Part of 17th International Conference on Human-Computer Interaction, HCI International 2015 - Los Angeles, United States
Duration: Aug 2 2015Aug 7 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9192
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Conference on Learning and Collaboration Technologies, LCT 2015 Held as Part of 17th International Conference on Human-Computer Interaction, HCI International 2015
CountryUnited States
CityLos Angeles
Period8/2/158/7/15

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Flanagan, B., Yin, C., Suzuki, T., & Hirokawa, S. (2015). Prediction of learner native language by writing error pattern. In P. Zaphiris, & A. Ioannou (Eds.), Learning and Collaboration Technologies - 2nd International Conference, LCT 2015 Held as Part of HCI International 2015, Proceedings (pp. 87-96). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9192). Springer Verlag. https://doi.org/10.1007/978-3-319-20609-7_9