GenERRate: generating errors for use in grammatical error detection

  • Authors:
  • Jennifer Foster;Øistein E. Andersen

  • Affiliations:
  • Dublin City University, Ireland;University of Cambridge, United Kingdom

  • Venue:
  • EdAppsNLP '09 Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper explores the issue of automatically generated ungrammatical data and its use in error detection, with a focus on the task of classifying a sentence as grammatical or ungrammatical. We present an error generation tool called GenERRate and show how GenERRate can be used to improve the performance of a classifier on learner data. We describe initial attempts to replicate Cambridge Learner Corpus errors using GenERRate.