Abstract
In this paper, we present our system developed for the SemEval 2016 Task 11: Complex Word Identification. Our team achieved the 3rd place among 21 participants. Our systems ranked 4th and 13th among 42 submitted systems. We proposed multiple features suitable for complex word identification, evaluated them, and discussed their properties. According to the results of our experiments, our final system used maximum entropy classifier with a single feature - document frequency.
Cite
CITATION STYLE
Konkol, M. (2016). UWB at SemEval-2016 task 11: Exploring features for complex word identification. In SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings (pp. 1038–1041). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s16-1162
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