PIHKers at CMCL 2021 Shared Task: Cosine Similarity and Surprisal to Predict Human Reading Patterns.
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.- Anthology ID:
- 2021.cmcl-1.12
- Volume:
- Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Emmanuele Chersoni, Nora Hollenstein, Cassandra Jacobs, Yohei Oseki, Laurent Prévot, Enrico Santus
- Venue:
- CMCL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 102–107
- Language:
- URL:
- https://preview.aclanthology.org/remove-affiliations/2021.cmcl-1.12/
- DOI:
- 10.18653/v1/2021.cmcl-1.12
- Cite (ACL):
- Lavinia Salicchi and Alessandro Lenci. 2021. PIHKers at CMCL 2021 Shared Task: Cosine Similarity and Surprisal to Predict Human Reading Patterns.. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 102–107, Online. Association for Computational Linguistics.
- Cite (Informal):
- PIHKers at CMCL 2021 Shared Task: Cosine Similarity and Surprisal to Predict Human Reading Patterns. (Salicchi & Lenci, CMCL 2021)
- PDF:
- https://preview.aclanthology.org/remove-affiliations/2021.cmcl-1.12.pdf