@inproceedings{agarwal-chatterjee-2021-langresearchlab,
    title = "{L}ang{R}esearch{L}ab{\_}{NC} at {CMCL}2021 Shared Task: Predicting Gaze Behaviour Using Linguistic Features and Tree Regressors",
    author = "Agarwal, Raksha  and
      Chatterjee, Niladri",
    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-1.8/",
    doi = "10.18653/v1/2021.cmcl-1.8",
    pages = "79--84",
    abstract = "Analysis of gaze data behaviour has gained momentum in recent years for different NLP applications. The present paper aims at modelling gaze data behaviour of tokens in the context of a sentence. We have experimented with various Machine Learning Regression Algorithms on a feature space comprising the linguistic features of the target tokens for prediction of five Eye-Tracking features. CatBoost Regressor performed the best and achieved fourth position in terms of MAE based accuracy measurement for the ZuCo Dataset."
}