2022
ACL
ACL 2022
HkAmsters at CMCL 2022 Shared Task: Predicting Eye-Tracking Data from a Gradient Boosting Framework with Linguistic Features
Abstract
AbstractEye movement data are used in psycholinguistic studies to infer information regarding cognitive processes during reading. In this paper, we describe our proposed method for the Shared Task of Cognitive Modeling and Computational Linguistics (CMCL) 2022 - Subtask 1, which involves data from multiple datasets on 6 languages. We compared different regression models using features of the target word and its previous word, and target word surprisal as regression features. Our final system, using a gradient boosting regressor, achieved the lowest mean absolute error (MAE), resulting in the best system of the competition.
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Interdisciplinary Bridge
— Interdisciplinary and Machine Learning and Natural Language Processing
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Hot Topic Early Bird
— cognitive modeling
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Regression
Interdisciplinary > Linguistics
Interdisciplinary > Linguistics > Computational Linguistics
Interdisciplinary > Cognitive Science > Cognitive Modeling
Machine Learning > Learning Types > Regression
Natural Language Processing > Applications > Text Processing