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1
On the Role of Low-Level Linguistic Tasks for Reading Time Prediction
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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2
On the Role of Low-level Linguistic Levels for Reading Time Prediction
In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021)
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3
The Reading Machine: a Versatile Framework for Studying Incremental Parsing Strategies
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
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)
Abstract: International audience ; In this paper we describe our contribution to the CMCL 2021 Shared Task, which consists in predicting 5 different eye tracking variables from English tokenized text. Our approach is based on a neural network that combines both raw textual features we extracted from the text and parser-based features that include linguistic predictions (e.g. part of speech) and complexity metrics (e.g., entropy of parsing). We found that both the features we considered as well as the architecture of the neural model that combined these features played a role in the overall performance. Our system achieved relatively high accuracy on the test data of the challenge and was ranked 2nd out of 13 competing teams and a total of 30 submissions.
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
URL: https://doi.org/10.18653/v1/2021.cmcl-1.13
https://hal.archives-ouvertes.fr/hal-03312501/file/2021.cmcl-1.13.pdf
https://hal.archives-ouvertes.fr/hal-03312501
https://hal.archives-ouvertes.fr/hal-03312501/document
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5
The Reading Machine: a Versatile Framework for Studying Incremental Parsing Strategies ...
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6
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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8
Annotation syntaxique automatique de la partie orale du CEFC ; Annotation syntaxique automatique de la partie orale du CÉFC
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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9
Annotation syntaxique automatique de la partie orale du ORFÉO
In: Langages, N 219, 3, 2020-08-11, pp.87-102 (2020)
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10
Typological Features for Multilingual Delexicalised Dependency Parsing
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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