LAST at CMCL 2021 Shared Task: Predicting Gaze Data During Reading with a Gradient Boosting Decision Tree Approach

A LightGBM model fed with target word lexical characteristics and features\nobtained from word frequency lists, psychometric data and bigram association\nmeasures has been optimized for the 2021 CMCL Shared Task on Eye-Tracking Data\nPrediction. It obtained the best performance of all teams on two of the five\neye-tracking measures to predict, allowing it to rank first on the official\nchallenge criterion and to outperform all deep-learning based systems\nparticipating in the challenge.\n