Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/89112
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dc.contributorDepartment of Chinese and Bilingual Studies-
dc.contributorDepartment of Computing-
dc.creatorLong, Y-
dc.creatorLu, Q-
dc.creatorXiang, R-
dc.creatorLi, M-
dc.creatorHuang, CR-
dc.date.accessioned2021-02-04T02:39:26Z-
dc.date.available2021-02-04T02:39:26Z-
dc.identifier.isbn9.78E+12-
dc.identifier.urihttp://hdl.handle.net/10397/89112-
dc.language.isoenen_US
dc.publisherAssociation for Computational Linguistics (ACL)en_US
dc.rights© 2017 Association for Computational Linguisticsen_US
dc.rightsACL materials are Copyright © 1963–2021 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 Long, Y., Lu, Q., Xiang, R., Li, M., & Huang, C. -. (2017). A cognition based attention model for sentiment analysis. Paper presented at the EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings, 462-471 is available at https://dx.doi.org/10.18653/v1/d17-1048en_US
dc.titleA cognition based attention model for sentiment analysisen_US
dc.typeConference Paperen_US
dc.identifier.spage462-
dc.identifier.epage471-
dc.identifier.doi10.18653/v1/d17-1048-
dcterms.abstractAttention models are proposed in sentiment analysis because some words are more important than others. However, most existing methods either use local context based text information or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. A reading prediction model is first built using eye-tracking data as dependent data and other features in the context as independent data. The predicted reading time is then used to build a cognition based attention (CBA) layer for neural sentiment analysis. As a comprehensive model, We can capture attentions of words in sentences as well as sentences in documents. Different attention mechanisms can also be incorporated to capture other aspects of attentions. Evaluations show the CBA based method outperforms the state-of-the-art local context based attention methods significantly. This brings insight to how cognition grounded data can be brought into NLP tasks.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIn Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, 9-11 September 2017, p. 462-471-
dcterms.issued2017-
dc.identifier.scopus2-s2.0-85048798787-
dc.relation.ispartofbookProceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, 9-11 September 2017-
dc.relation.conferenceConference on Empirical Methods in Natural Language Processing [EMNLP]-
dc.description.validate202101 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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