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Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors.

Conference paper
Authors Ildikó Pilán
Elena Volodina
Published in Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, COLING, Santa Fe, New Mexico, USA, August 25, 2018.
ISBN 978-1-948087-61-2
Publisher Association of Computation Linguistics
Publication year 2018
Published at Department of Swedish
Language en
Keywords second language learner misspellings, error detection, error correction, correction candidate ranking
Subject categories General Language Studies and Linguistics, Specific Languages, Language Technology (Computational Linguistics)


The presence of misspellings and other errors or non-standard word forms poses a consider- able challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We in- vestigate, therefore, an unsupervised method where correction candidates for Swedish language learners’ errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors.

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