CLaC at SemEval-2016 task 11: Exploring linguistic and psycho-linguistic features for complex word identification

Abstract

This paper describes the system deployed by the CLaC-EDLK team to the SemEval 2016, Complex Word Identification task. The goal of the task is to identify if a given word in a given context is simple or complex. Our system relies on linguistic features and cognitive complexity. We used several supervised models, however the Random Forest model outperformed the others. Overall our best configuration achieved a G-score of 68.8% in the task, ranking our system 21 out of 45.