Article | Proceedings of the third workshop on NLP for computer-assisted language learning at SLTC 2014, Uppsala University | Improving Collocation Correction by Ranking Suggestions Using Linguistic Knowledge
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Title:
Improving Collocation Correction by Ranking Suggestions Using Linguistic Knowledge
Author:
Roberto Carlini: Natural Language Processing Group, Department of Information and Communication Technologies Pompeu Fabra University, Barcelona, Spain Joan Codina-Filba: Natural Language Processing Group, Department of Information and Communication Technologies Pompeu Fabra University, Barcelona, Spain Leo Wanner: Catalan Institute for Research and Advanced Studies (ICREA)/Natural Language Processing Group, Department of Information and Communication Technologies Pompeu Fabra University, Barcelona, Spain
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Full text (pdf)
Year:
2014
Conference:
Proceedings of the third workshop on NLP for computer-assisted language learning at SLTC 2014, Uppsala University
Issue:
107
Article no.:
001
Pages:
1–12
No. of pages:
12
Publication type:
Abstract and Fulltext
Published:
2014-11-11
ISBN:
978-91-7519-175-1
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Series:
NEALT Proceedings Series
Publisher:
Linköping University Electronic Press, Linköpings universitet


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The importance of collocations in the context of second language learning is generally acknowledged. Studies show that the “collocation density" in learner corpora is nearly the same as in native corpora, i.e., that use of collocations by learners is as common as it is by native speakers, while the collocation error rate in learner corpora is about ten times as high as in native reference corpora. Therefore, CALL could be of great aid to support the learners for better mastering of collocations. However, surprisingly few works address specifically research on CALL-oriented collocation learning assistants that detect miscollocations in the writings of the learners and propose suggestions for their correction or that offer the learner the possibility to verify a word co-occurrence with respect to its correctness as collocation and obtain suggestions for its correction in case it is determined to be a miscollocation. This disregard is likely to be, on the one hand, due to the focus of the CALL research so far on grammatical matters, and, on the other hand, due to the complexity of the problem. In order to be able to provide an adequate correction of a miscollocation, the collocation learning assistant must “guess" the meaning that the learner intended to express. This makes it very different from grammar or spell checkers, which can draw on grammatical respectively orthographic regularities of a language. In this paper, we focus on the problem of the provision of a ranked list of correction suggestions in a context in which the learner submits a collocation for verification and obtains a list of correction suggestions in the case of a miscollocation. We show that the retrieval of the suggestions and their ranking benefits greatly from NLP techniques that provide the syntactic dependency structure and subcategorization information of the word co-occurrences and a weighted Pointwise Mutual Information (PMI) that reflects the fact that in a collocation, it is the base that is subject of the free choice of the speaker, while the occurrence of the collocate is restricted by the base, i.e., that collocations are per se asymmetric.

Keywords: CALL; collocations; miscollocation correction; syntactic dependencies; sub-categorization; PMI

Proceedings of the third workshop on NLP for computer-assisted language learning at SLTC 2014, Uppsala University

Author:
Roberto Carlini, Joan Codina-Filba, Leo Wanner
Title:
Improving Collocation Correction by Ranking Suggestions Using Linguistic Knowledge
References:

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Proceedings of the third workshop on NLP for computer-assisted language learning at SLTC 2014, Uppsala University

Author:
Roberto Carlini, Joan Codina-Filba, Leo Wanner
Title:
Improving Collocation Correction by Ranking Suggestions Using Linguistic Knowledge
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