@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://aclanthology.org/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.",
}
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%0 Conference Proceedings
%T LangResearchLab_NC at CMCL2021 Shared Task: Predicting Gaze Behaviour Using Linguistic Features and Tree Regressors
%A Agarwal, Raksha
%A Chatterjee, Niladri
%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 agarwal-chatterjee-2021-langresearchlab
%X 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.
%R 10.18653/v1/2021.cmcl-1.8
%U https://aclanthology.org/2021.cmcl-1.8
%U https://doi.org/10.18653/v1/2021.cmcl-1.8
%P 79-84
Markdown (Informal)
[LangResearchLab_NC at CMCL2021 Shared Task: Predicting Gaze Behaviour Using Linguistic Features and Tree Regressors](https://aclanthology.org/2021.cmcl-1.8) (Agarwal & Chatterjee, CMCL 2021)
ACL