This paper revisits the problem of complex word identification (CWI) following up the SemEval CWI shared task. We use ensemble classifiers to investigate how well computational methods can discriminate between complex and non-complex words. Furthermore, we analyze the classification performance to understand what makes lexical complexity challenging. Our findings show that most systems performed poorly on the SemEval CWI dataset, and one of the reasons for that is the way in which human annotation was performed.
@article{arxiv.1710.04989,
title = {Complex Word Identification: Challenges in Data Annotation and System Performance},
author = {Marcos Zampieri and Shervin Malmasi and Gustavo Paetzold and Lucia Specia},
journal= {arXiv preprint arXiv:1710.04989},
year = {2017}
}
Comments
Proceedings of the 4th Workshop on NLP Techniques for Educational Applications (NLPTEA 2017)