Exploring natural language processing for single-word and multi-word lexical complexity from a second language learner perspective
In this thesis, we investigate how natural language processing (NLP) tools and techniques can be applied to vocabulary aimed at second language learners of Swedish in order to classify vocabulary items into different proficiency levels suitable for learners of different levels. In the first part, we use feature-engineering to represent words as vectors and feed these vectors into machine learning algorithms in order to (1) learn CEFR labels from the input data and (2) predict the CEFR level of unseen words. Our experiments corroborate the finding that feature-based classification models using 'traditional' machine learning still outperform deep learning architectures in the task of deciding how complex a word is. In the second part, we use crowdsourcing as a technique to generate ranked lists of multi-word expressions using both experts and non-experts (i.e. language learners). Our experiment shows that non-expert and expert rankings are highly correlated, suggesting that non-expert intuition can be seen as on-par with expert knowledge, at least in the chosen experimental configuration. The main practical output of this research comes in two forms: prototypes and resources. We have implemented various prototype applications for (1) the automatic prediction of words based on the feature-engineering machine learning method, (2) language learning applications using graded word lists, and (3) an annotation tool for the manual annotation of expressions across a variety of linguistic factors.
Parts of work
Alfter, David and Yuri Bizzoni and Anders Agebjörn and Elena Volodina and Ildikó Pilán 2016. From distributions to labels: A lexical proficiency analysis using learner corpora. Proceedings of the joint workshop on NLP for Computer Assisted Language Learning and NLP for Language Acquisition at SLTC, Umeå, 16th November 2016 (No. 130, pp. 1-7). Linköping University Electronic Press.Alfter, David and Elena Volodina 2018. Towards single word lexical complexity prediction. Proceedings of the thirteenth workshop on innovative use of NLP for building educational applications (pp. 79-88).Alfter, David and Ildikó Pilán 2018. SB@GU at the Complex Word Identification 2018 Shared Task. Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications (pp. 315-321).Alfter, David and Therese Lindström Tiedemann and Elena Volodina 2020. Crowdsourcing Relative Rankings of Multi-Word Expressions: Experts versus Non-Experts. Northern European Journal of Language Technology.Alfter, David and Therese Lindström Tiedemann and Elena Volodina 2019. LEGATO: A flexible lexicographic annotation tool. In NEAL Proceedings of the 22nd Nordic Conference on Computional Linguistics (NoDaLiDa), September 30-October 2, Turku, Finland (No. 167, pp. 382-388). Linköping University Electronic Press.Graën, Johannes and David Alfter and Gerold Schneider 2020. Using Multilingual Resources to Evaluate CEFRLex for Learner Applications. Proceedings of The 12th Language Resources and Evaluation Conference (pp.346-355).Alfter, David and Lars Borin and Ildikó Pilán and Therese Lindström Tiedemann and Elena Volodina 2019. Lärka: From Language Learning Platform to Infrastructure for Research on Language Learning. In Selected papers from the CLARIN Annual Conference 2018, Pisa, 8-10 October 2018 (No. 159, pp. 1-14). Linköping University Electronic Press.Alfter, David and Johannes Graën 2019. Interconnecting lexical resources and word alignment: How do learners get on with particle verbs?. In Proceedings of the 22nd Nordic Conference on Computational Linguistics (pp. 321-326).
Doctor of Philosophy
Göteborgs universitet. Humanistiska fakulteten
University of Gothenburg. Faculty of Humanities
Department of Swedish ; Institutionen för svenska språket
Tisdag den 2 mars 2021, kl 13, hörsal J330, Humaninsten, Renströmsgatan
Date of defence
natural language processing
second language learning