USAAR at SemEval-2016 Task 11: Complex Word Identification with Sense Entropy and Sentence Perplexity
José Manuel Martínez Martínez, Liling Tan · 2016
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.