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Judging grammaticality: experiments in sentence classification

Wagner, Joachim orcid logoORCID: 0000-0002-8290-3849, Foster, Jennifer orcid logoORCID: 0000-0002-7789-4853 and van Genabith, Josef orcid logoORCID: 0000-0003-1322-7944 (2009) Judging grammaticality: experiments in sentence classification. CALICO Journal, 26 (3). pp. 474-490. ISSN 0742-7778

Abstract
A classifier which is capable of distinguishing a syntactically well formed sentence from a syntactically ill formed one has the potential to be useful in an L2 language-learning context. In this article, we describe a classifier which classifies English sentences as either well formed or ill formed using information gleaned from three different natural language processing techniques. We describe the issues involved in acquiring data to train such a classifier and present experimental results for this classifier on a variety of ill formed sentences. We demonstrate that (a) the combination of information from a variety of linguistic sources is helpful, (b) the trade-off between accuracy on well formed sentences and accuracy on ill formed sentences can be fine tuned by training multiple classifiers in a voting scheme, and (c) the performance of the classifier is varied, with better performance on transcribed spoken sentences produced by less advanced language learners.
Metadata
Item Type:Article (Published)
Refereed:Yes
Subjects:Computer Science > Computational linguistics
Computer Science > Machine learning
Humanities > Language
DCU Faculties and Centres:Research Initiatives and Centres > National Centre for Language Technology (NCLT)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Publisher:The Computer Assisted Language Instruction Consortium
Official URL:https://www.calico.org/memberBrowse.php?action=art...
Copyright Information:© 2009 CALICO Journal
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Irish Research Council for Science Engineering and Technology, Embark Initiative postdoctoral fellowship P/04/232
ID Code:15662
Deposited On:18 Aug 2010 10:10 by Joachim Wagner . Last Modified 21 Jan 2022 16:29
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