@inproceedings{bestgen-2021-last,
    title = "{LAST} at {CMCL} 2021 Shared Task: Predicting Gaze Data During Reading with a Gradient Boosting Decision Tree Approach",
    author = "Bestgen, Yves",
    editor = "Chersoni, Emmanuele  and
      Hollenstein, Nora  and
      Jacobs, Cassandra  and
      Oseki, Yohei  and
      Pr{\'e}vot, Laurent  and
      Santus, Enrico",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.cmcl-1.10/",
    doi = "10.18653/v1/2021.cmcl-1.10",
    pages = "90--96",
    abstract = "A LightGBM model fed with target word lexical characteristics and features obtained from word frequency lists, psychometric data and bigram association measures has been optimized for the 2021 CMCL Shared Task on Eye-Tracking Data Prediction. It obtained the best performance of all teams on two of the five eye-tracking measures to predict, allowing it to rank first on the official challenge criterion and to outperform all deep-learning based systems participating in the challenge."
}