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Reinvestigating the Classification Approach to the Article and Preposition Error Correction

  • Roman GrundkiewiczEmail author
  • Marcin Junczys-Dowmunt
Conference paper
  • 304 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10930)

Abstract

In this work, we reinvestigate the classifier-based approach to article and preposition error correction going beyond linguistically motivated factors. We show that state-of-the-art results can be achieved without relying on a plethora of heuristic rules, complex feature engineering and advanced NLP tools. A proposed method for detecting spaces for article insertion is even more efficient than methods that use a parser. We examine automatically trained word classes acquired by unsupervised learning as a substitution for commonly used part-of-speech tags. Our best models significantly outperform the top systems from CoNLL-2014 Shared Task in terms of article and preposition error correction.

Keywords

Grammatical error correction Article errors Preposition errors CoNLL-2014 shared task Detecting omitted words 

Notes

Acknowledgements

This work has been funded by the National Science Centre, Poland (Grant No. 2014/15/N/ST6/02330).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Adam Mickiewicz UniversityPoznańPoland

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