Complex word identification based on frequency in a learner corpus


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.

Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications (BEA 13)
Tomoyuki Kajiwara
Tomoyuki Kajiwara
Guest Assistant Professor

Natural Language Processing. Especially: Text Simplification, Paraphrasing, Semantic Textual Similarity, Quality Estimation.