Complex Word Identification Based on Frequency in a Learner Corpus

Tomoyuki Kajiwara, Mamoru Komachi


Abstract
We introduce the TMU systems for the Complex Word Identification (CWI) Shared Task 2018. TMU systems use random forest classifiers and regressors whose features are the number of characters, the number of words, and the frequency of target words in various corpora. Our simple systems performed best on 5 tracks out of 12 tracks. Our ablation analysis revealed the usefulness of a learner corpus for CWI task.
Anthology ID:
W18-0521
Volume:
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venues:
BEA | NAACL | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
195–199
Language:
URL:
https://www.aclweb.org/anthology/W18-0521
DOI:
10.18653/v1/W18-0521
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-0521.pdf