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On the Role of Low-Level Linguistic Tasks for Reading Time Prediction
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In: Proceedings of the Annual Meeting of the Cognitive Science Society, 43(43) ; 43rd Annual Meeting of the Cognitive Science Society ; https://hal.archives-ouvertes.fr/hal-03303689 ; 43rd Annual Meeting of the Cognitive Science Society, Jul 2021, Vienna, Austria. pp.452 ; https://cognitivesciencesociety.org/cogsci-2021/ (2021)
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Abstract:
International audience ; It has been shown that complexity metrics, computed by a syntactic parser, is a predictor of human reading time, which is an approximation of human sentence comprehension difficulty. Nevertheless, parsers usually take as input sentences that have already been processed or even manually annotated. We propose to study a more realistic scenario, where the various processing levels (tokenization, PoS and morphology tagging, lemmatization, syntactic parsing and sentence segmentation) are predicted incrementally from raw text. To this end, we propose a versatile modeling framework, we call the Reading Machine, that performs all such linguistic tasks and allows to incorporate cognitive constrains such as incrementality. We illustrate the behavior of this setting through a case study where we test the hypothesis that the complexity metrics computed at different processing levels predicts human reading difficulty, and that when cognitive constraints are applied to the machine (e.g., incrementality), it yields better predictions.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences; cognitive modeling; entropy; reading time; surprisal
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URL: https://hal.archives-ouvertes.fr/hal-03303689
https://hal.archives-ouvertes.fr/hal-03303689/file/COGSCI_2021.pdf
https://hal.archives-ouvertes.fr/hal-03303689/document
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On the Role of Low-level Linguistic Levels for Reading Time Prediction
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In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021)
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The Reading Machine: a Versatile Framework for Studying Incremental Parsing Strategies
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In: The 17th International Conference on Parsing Technologies ; https://hal.archives-ouvertes.fr/hal-03328439 ; The 17th International Conference on Parsing Technologies, Aug 2021, Bangkok (virtual), Thailand (2021)
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TALEP at CMCL 2021 Shared Task: Non Linear Combination of Low and High-Level Features for Predicting Eye-Tracking Data
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In: Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics ; Workshop on Cognitive Modeling and Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03312501 ; Workshop on Cognitive Modeling and Computational Linguistics, Association for Computational Linguistics, Jun 2021, Online, Mexico. pp.108-113, ⟨10.18653/v1/2021.cmcl-1.13⟩ (2021)
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The Reading Machine: a Versatile Framework for Studying Incremental Parsing Strategies ...
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On the Role of Low-level Linguistic Levels for Reading Time Prediction ...
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On the Role of Low-level Linguistic Levels for Reading Time Prediction ...
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Annotation syntaxique automatique de la partie orale du CEFC ; Annotation syntaxique automatique de la partie orale du CÉFC
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In: ISSN: 0458-726X ; EISSN: 1958-9549 ; Langages ; https://hal.archives-ouvertes.fr/hal-02973242 ; Langages, Armand Colin (Larousse jusqu'en 2003), 2020 (2020)
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Annotation syntaxique automatique de la partie orale du ORFÉO
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In: Langages, N 219, 3, 2020-08-11, pp.87-102 (2020)
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Typological Features for Multilingual Delexicalised Dependency Parsing
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In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; https://hal.archives-ouvertes.fr/hal-02278897 ; 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Jun 2019, Minneapolis, United States. pp.3919-3930, ⟨10.18653/v1/N19-1393⟩ (2019)
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