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        <title>Leveraging Native Data to Correct Preposition Errors in Learners’ Dutch</title>
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    <abstract>We address the task of automatically correcting preposition errors in learners’ Dutch by modelling preposition usage in native language. Specifically, we build two models exploiting a large corpus of Dutch. The first is a binary model for detecting whether a preposition should be used at all in a given position or not. The second is a multiclass model for selecting the appropriate preposition in case one should be used. The models are tested on native as well as learners data. For the latter we exploit a crowdsourcing strategy to elicit native judgements. On native test data the models perform very well, showing that we can model preposition usage appropriately. However, the evaluation on learners’ data shows that while detecting that a given preposition is wrong is doable reasonably well, detecting the absence of a preposition is a lot more difficult. Observing such results and the data we deal with, we envisage various ways of improving performance, and report them in the final section of this article.</abstract>
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%0 Conference Proceedings
%T Leveraging Native Data to Correct Preposition Errors in Learners’ Dutch
%A Kloppenburg, Lennart
%A Nissim, Malvina
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F kloppenburg-nissim-2016-leveraging
%X We address the task of automatically correcting preposition errors in learners’ Dutch by modelling preposition usage in native language. Specifically, we build two models exploiting a large corpus of Dutch. The first is a binary model for detecting whether a preposition should be used at all in a given position or not. The second is a multiclass model for selecting the appropriate preposition in case one should be used. The models are tested on native as well as learners data. For the latter we exploit a crowdsourcing strategy to elicit native judgements. On native test data the models perform very well, showing that we can model preposition usage appropriately. However, the evaluation on learners’ data shows that while detecting that a given preposition is wrong is doable reasonably well, detecting the absence of a preposition is a lot more difficult. Observing such results and the data we deal with, we envisage various ways of improving performance, and report them in the final section of this article.
%U https://aclanthology.org/L16-1449
%P 2819-2824

Markdown (Informal)

[Leveraging Native Data to Correct Preposition Errors in Learners’ Dutch](https://aclanthology.org/L16-1449) (Kloppenburg & Nissim, LREC 2016)

• Leveraging Native Data to Correct Preposition Errors in Learners’ Dutch (Kloppenburg & Nissim, LREC 2016)

ACL

• Lennart Kloppenburg and Malvina Nissim. 2016. Leveraging Native Data to Correct Preposition Errors in Learners’ Dutch. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 2819–2824, Portorož, Slovenia. European Language Resources Association (ELRA).