Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/95826
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dc.contributorDepartment of Chinese and Bilingual Studiesen_US
dc.creatorSalicchi, Len_US
dc.creatorXiang, Ren_US
dc.creatorHsu, YYen_US
dc.date.accessioned2022-10-17T08:19:04Z-
dc.date.available2022-10-17T08:19:04Z-
dc.identifier.isbn978-1-955917-29-2en_US
dc.identifier.urihttp://hdl.handle.net/10397/95826-
dc.language.isoenen_US
dc.publisherAssociation for Computational Linguisticsen_US
dc.rights©2022 Association for Computational Linguisticsen_US
dc.rightsACL materials are Copyright © 1963–2022 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Lavinia Salicchi, Rong Xiang, and Yu-Yin Hsu. 2022. HkAmsters at CMCL 2022 Shared Task: Predicting Eye-Tracking Data from a Gradient Boosting Framework with Linguistic Features. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 114–120, Dublin, Ireland. Association for Computational Linguistics is available at https://aclanthology.org/2022.cmcl-1.13/en_US
dc.titleHkAmsters at CMCL 2022 shared task : predicting eye-tracking data from a gradient boosting framework with linguistic featuresen_US
dc.typeConference Paperen_US
dc.identifier.spage114en_US
dc.identifier.epage120en_US
dcterms.abstractEye movement data are used in psycholinguis-tic studies to infer information regarding cogni-tive processes during reading. In this paper, wedescribe our proposed method for the SharedTask of Cognitive Modeling and ComputationalLinguistics (CMCL) 2022 - Subtask 1, whichinvolves data from multiple datasets on 6 lan-guages. We compared different regression mod-els using features of the target word and itsprevious word, and target word surprisal as re-gression features. Our final system, using agradient boosting regressor, achieved the low-est mean absolute error (MAE), resulting in thebest system of the competition.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationWorkshop on Cognitive Modeling and Computational Linguistics (CMCL 2022), Dublin, Ireland, 26 May 2022, p. 114-120en_US
dcterms.issued2022-05-26-
dc.relation.conferenceWorkshop on Cognitive Modeling and Computational Linguistics [CMCL]en_US
dc.publisher.placeStroudsburg, PAen_US
dc.description.validate202210 bcchen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera1795-
dc.identifier.SubFormID45960-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextFaculty of Humanity at PolyU (project code: BD8S)en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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