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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 toidentify if a given word in a given context is "simple" or "complex". Oursystem relies on linguistic features and cognitive complexity. We used severalsupervised models, however the Random Forest model outperformed the others.Overall our best configuration achieved a G-score of 68.8% in the task, rankingour system 21 out of 45.

CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification