%0 Conference Proceedings %T CompNA at SemEval-2021 Task 1: Prediction of lexical complexity analyzing heterogeneous features %A Vettigli, Giuseppe %A Sorgente, Antonio %Y Palmer, Alexis %Y Schneider, Nathan %Y Schluter, Natalie %Y Emerson, Guy %Y Herbelot, Aurelie %Y Zhu, Xiaodan %S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021) %D 2021 %8 August %I Association for Computational Linguistics %C Online %F vettigli-sorgente-2021-compna %X This paper describes the CompNa model that has been submitted to the Lexical Complexity Prediction (LCP) shared task hosted at SemEval 2021 (Task 1). The solution is based on combining features of different nature through an ensambling method based on Decision Trees and trained using Gradient Boosting. We discuss the results of the model and highlight the features with more predictive capabilities. %R 10.18653/v1/2021.semeval-1.69 %U https://aclanthology.org/2021.semeval-1.69 %U https://doi.org/10.18653/v1/2021.semeval-1.69 %P 560-564