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This paper describes an information-theoretic approach to complex word identification using a classifier based on an entropy based measure based on word senses and sentence-level perplexity features. We describe the motivation behind these features based on information density and demonstrate that they perform modestly well in the complex word identification task in SemEval-2016. We also discuss the possible improvements that can be made to future work by exploring the subjectivity of word complexity and more robust evaluation metrics for the complex word identification task.

USAAR at SemEval-2016 Task 11: Complex Word Identification with Sense Entropy and Sentence Perplexity