Native Language Identification Using Large, Longitudinal Data

Xiao Jiang, Yufan Guo, Jeroen Geertzen, Dora Alexopoulou, Lin Sun, Anna Korhonen


Abstract
Native Language Identification (NLI) is a task aimed at determining the native language (L1) of learners of second language (L2) on the basis of their written texts. To date, research on NLI has focused on relatively small corpora. We apply NLI to the recently released EFCamDat corpus which is not only multiple times larger than previous L2 corpora but also provides longitudinal data at several proficiency levels. Our investigation using accurate machine learning with a wide range of linguistic features reveals interesting patterns in the longitudinal data which are useful for both further development of NLI and its application to research on L2 acquisition.
Anthology ID:
L14-1051
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3309–3312
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/1068_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Xiao Jiang, Yufan Guo, Jeroen Geertzen, Dora Alexopoulou, Lin Sun, and Anna Korhonen. 2014. Native Language Identification Using Large, Longitudinal Data. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3309–3312, Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):
Native Language Identification Using Large, Longitudinal Data (Jiang et al., LREC 2014)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/1068_Paper.pdf