Automatic detection of unexpected/erroneous collocations in learner corpus

Jen-Yu Li, Thomas Gaillat


Abstract
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.
Anthology ID:
2020.mwe-1.13
Volume:
Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons
Month:
December
Year:
2020
Address:
online
Venue:
MWE
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
101–106
Language:
URL:
https://aclanthology.org/2020.mwe-1.13
DOI:
Bibkey:
Cite (ACL):
Jen-Yu Li and Thomas Gaillat. 2020. Automatic detection of unexpected/erroneous collocations in learner corpus. In Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons, pages 101–106, online. Association for Computational Linguistics.
Cite (Informal):
Automatic detection of unexpected/erroneous collocations in learner corpus (Li & Gaillat, MWE 2020)
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PDF:
https://aclanthology.org/2020.mwe-1.13.pdf