Jen-Yu Li

Also published as: Jen Yu Li


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A new learner language data set for the study of English for Specific Purposes at university
Cyriel Mallart | Nicolas Ballier | Jen-Yu Li | Andrew Simpkin | Bernardo Stearns | Rémi Venant | Thomas Gaillat
Proceedings of the 4th Conference on Language, Data and Knowledge

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Exploring a New Grammatico-functional Type of Measure as Part of a Language Learning Expert System
Cyriel Mallart | Andrew Simpkin | Rmi Venant | Nicolas Ballier | Bernardo Stearns | Jen Yu Li | Thomas Gaillat
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)

This paper explores the use of L2-specific grammatical microsystems as elements of the domain knowledge of an Intelligent Computer-assisted Language Learning (ICALL) system. We report on the design of new grammatico-functional measures and their association with proficiency. We illustrate the approach with the design of the IT, THIS, THAT proform microsystem. The measures rely on the paradigmatic relations between words of the same linguistic functions. They are operationalised with one frequency-based and two probabilistic methods, i.e., the relative proportions of the forms and their likelihood of occurrence. Ordinal regression models show that the measures are significant in terms of association with CEFR levels, paving the way for their introduction in a specific proform microsystem expert model.


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Automatic detection of unexpected/erroneous collocations in learner corpus
Jen-Yu Li | Thomas Gaillat
Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons

This research investigates the collocational errors made by English learners in a learner corpus. It focuses on the extraction of unexpected collocations. A system was proposed and implemented with open source toolkit. Firstly, the collocation extraction module was evaluated by a corpus with manually annotated collocations. Secondly, a standard collocation list was collected from a corpus of native speaker. Thirdly, a list of unexpected collocations was generated by extracting candidates from a learner corpus and discarding the standard collocations on the list. The overall performance was evaluated, and possible sources of error were pointed out for future improvement.