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Automatic proficiency level prediction for Intelligent Computer-Assisted Language Learning


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Title: Automatic proficiency level prediction for Intelligent Computer-Assisted Language Learning
Authors: Pilán, Ildikó
E-mail: ildiko.pilan@gmail.com
Issue Date: 17-May-2018
University: Göteborgs universitet. Humanistiska fakulteten
University of Gothenburg. Faculty of Arts
Institution: Department of Swedish ; Institutionen för svenska språket
Parts of work: Pilán, Ildikó, Sowmya Vajjala and Elena Volodina 2016. A readable read: automatic assessment of language learning materials based on linguistic complexity. International Journal of Computational Linguistics and Applications (IJLCA) 7 (1): 143–159.

Pilán, Ildikó 2016. Detecting Context Dependence in Exercise Item Candidates Selected from Corpora. In Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications (BEA), 151–161.

Pilán, Ildikó, Elena Volodina and Lars Borin 2017. Candidate sentence selection for language learning exercises: from a comprehensive framework to an empirical evaluation. Traitement Automatique des Langues (TAL) Journal, Special issue on NLP for learning and teaching 57 (3): 67–91.

Pilán, Ildikó, Elena Volodina and Torsten Zesch 2016. Predicting proficiency levels in learner writings by transferring a linguistic complexity model from expert-written coursebooks. Proceedings of the 26th International Conference on Computational Linguistics (COLING), 2101–2111.

Pilán, Ildikó, David Alfter and Elena Volodina 2016. Coursebook texts as a helping hand for classifying linguistic complexity in language learners’ writings. Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC), 120–126.

Pilán, Ildikó and Elena Volodina. Investigating the importance of linguistic complexity features across different datasets related to language learning. Submitted.
Date of Defence: 2018-06-14
Disputation: 13.15, Stora hörsalen (2150), Eklandagatan 86
Degree: Doctor of Philosophy
Publication type: Doctoral thesis
Series/Report no.: Data Linguistica
29
Keywords: natural language processing
linguistic complexity
readability
CEFR
second language learning
corpus examples
text classification
machine learning
domain adaptation
Abstract: With the ever-growing presence of electronic devices in our everyday lives, it is compelling to investigate how technology can contribute to make our language learning process more efficient and enjoyable. A fundamental piece in this puzzle is the ability to measure the complexity of the language that learners are able to deal with and produce at different stages of their progress. In this thesis work, we explore automatic approaches for modeling linguistic complexity at different levels of ... more
ISBN: 978-91-87850-68-4
ISSN: 0347-948X
URI: http://hdl.handle.net/2077/55895
Appears in Collections:Doctoral Theses from University of Gothenburg / Doktorsavhandlingar från Göteborgs universitet
Doctoral Theses / Doktorsavhandlingar Institutionen för svenska språket

 

 

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