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Integrating learner corpora and natural language processing: A crucial step towards reconciling technological sophistication and pedagogical effectiveness1

Published online by Cambridge University Press:  24 August 2007

Sylviane Granger ,
Olivier Kraif ,
Claude Ponton ,
Georges Antoniadis  and
Virginie Zampa
Sylviane Granger
Affiliation:
Université catholique de Louvain, Centre for English Corpus Linguistics, Place Blaise Pascal 1, B-1348 Louvain-la-Neuve, Belgium (email: sylviane.granger@uclouvain.be)
Olivier Kraif
Affiliation:
LIDILEM, Université Stendhal Grenoble3, BP-25 – 38040 Grenoble Cedex 9France (email: Olivier.Kraif@u-grenoble3.fr)
Claude Ponton
Affiliation:
LIDILEM, Université Stendhal Grenoble3, BP-25 – 38040 Grenoble Cedex 9France (email: Claude.Ponton@u-grenoble3.fr)
Georges Antoniadis
Affiliation:
LIDILEM, Université Stendhal Grenoble3, BP-25 – 38040 Grenoble Cedex 9France (email: Georges.Antoniadis@u-grenoble3.fr)
Virginie Zampa
Affiliation:
LIDILEM, Université Stendhal Grenoble3, BP-25 – 38040 Grenoble Cedex 9France (email: Virgine.Zampa@u-grenoble3.fr)
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Abstract

Learner corpora, electronic collections of spoken or written data from foreign language learners, offer unparalleled access to many hitherto uncovered aspects of learner language, particularly in their error-tagged format. This article aims to demonstrate the role that the learner corpus can play in CALL, particularly when used in conjunction with web-based interfaces which provide flexible access to error-tagged corpora that have been enhanced with simple NLP techniques such as POS-tagging or lemmatization and linked to a wide range of learner and task variables such as mother tongue background or activity type. This new resource is of interest to three main types of users: teachers wishing to prepare pedagogical materials that target learners' attested difficulties; learners themselves for editing or language awareness purposes and NLP researchers, for whom it serves as a benchmark for testing automatic error detection systems.

Keywords

Learner corporaerror taggingerror detectionNLPweb interfaceFrench
Type
Research Article
Information
ReCALL , Volume 19 , Issue 3 , September 2007 , pp. 252 - 268
Copyright
Copyright © Cambridge University Press 2007

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References

1 The research reported in this article is part of a wider project on Integrated Digital Language Learning (IDILL) carried out within the framework of the EU-funded network of excellence Kaleidoscope dedicated to research in the field of technology-enhanced learning: http://www.noe-kaleidoscope.org/pub/