%0 Conference Proceedings %T PIHKers at CMCL 2021 Shared Task: Cosine Similarity and Surprisal to Predict Human Reading Patterns. %A Salicchi, Lavinia %A Lenci, Alessandro %Y Chersoni, Emmanuele %Y Hollenstein, Nora %Y Jacobs, Cassandra %Y Oseki, Yohei %Y Prévot, Laurent %Y Santus, Enrico %S Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics %D 2021 %8 June %I Association for Computational Linguistics %C Online %F salicchi-lenci-2021-pihkers %X 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. %R 10.18653/v1/2021.cmcl-1.12 %U https://aclanthology.org/2021.cmcl-1.12 %U https://doi.org/10.18653/v1/2021.cmcl-1.12 %P 102-107