@inproceedings{salicchi-lenci-2021-pihkers,
    title = "{PIHK}ers at {CMCL} 2021 Shared Task: Cosine Similarity and Surprisal to Predict Human Reading Patterns.",
    author = "Salicchi, Lavinia  and
      Lenci, Alessandro",
    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-1.12/",
    doi = "10.18653/v1/2021.cmcl-1.12",
    pages = "102--107",
    abstract = "Eye-tracking psycholinguistic studies have revealed that context-word semantic coherence and predictability influence language processing. In this paper we show our approach to predict eye-tracking features from the ZuCo dataset for the shared task of the Cognitive Modeling and Computational Linguistics (CMCL2021) workshop. Using both cosine similarity and surprisal within a regression model, we significantly improved the baseline Mean Absolute Error computed among five eye-tracking features."
}