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From FreEM to D'AlemBERT ; From FreEM to D'AlemBERT: a Large Corpus and a Language Model for Early Modern French
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In: Proceedings of the 13th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-03596653 ; Proceedings of the 13th Language Resources and Evaluation Conference, European Language Resources Association, Jun 2022, Marseille, France (2022)
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Hippocampal and auditory contributions to speech segmentation
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In: ISSN: 0010-9452 ; Cortex ; https://hal.archives-ouvertes.fr/hal-03604957 ; Cortex, Elsevier, 2022, ⟨10.1016/j.cortex.2022.01.017⟩ (2022)
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The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Slovenian 1.3
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FiNER-139: A Financial Numeric Entity Recognition Dataset ...
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FiNER-139: A Financial Numeric Entity Recognition Dataset ...
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Estimating the Emotional Information in Japanese Songs Using Search Engines
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In: Sensors; Volume 22; Issue 5; Pages: 1800 (2022)
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Joint learning of morphology and syntax with cross-level contextual information flow
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In: 2022 ; 1 ; 33 (2022)
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Corpus Linguistics approaches to trainee translators’ framing practice in news translation
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In: Translation and Interpreting : the International Journal of Translation and Interpreting Research, Vol 12 , Iss 1 (2022) (2022)
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Dissociated face- and word-selective intracerebral responses in the human ventral occipito-temporal cortex
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In: ISSN: 1863-2653 ; EISSN: 1863-2661 ; Brain Structure and Function ; https://hal.archives-ouvertes.fr/hal-03437752 ; Brain Structure and Function, Springer Verlag, 2021, 226 (9), pp.3031-3049. ⟨10.1007/s00429-021-02350-4⟩ (2021)
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Critical information thresholds underlying generic and familiar face categorisation at the same face encounter
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In: ISSN: 1053-8119 ; EISSN: 1095-9572 ; NeuroImage ; https://hal.archives-ouvertes.fr/hal-03437754 ; NeuroImage, Elsevier, 2021, 243, pp.118481. ⟨10.1016/j.neuroimage.2021.118481⟩ (2021)
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Exploring the Relationship Between Different Smartphone Reading Annotation Strategies and Students’ Comprehension
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In: Boise State University Theses and Dissertations (2021)
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Abstract:
This study explores the relationship between different smartphone reading annotation strategies and students’ comprehension. Subjects in the study are 139 teenage students enrolled in a religion class in the Southwestern United States. Each of the participants utilized a digital reading app on their personal smartphone to read an 842-word religious text. Subjects were encouraged to look for, highlight, or tag passages in the text that they felt were important to understanding the meaning of the text. After completing the reading, participants completed a multiple-choice quiz with both factual and inferential questions and wrote a short essay on how they felt the text could be used to resolve an issue in their personal life. The researcher analyzed the data by comparing the frequency of tags and highlights each subject created with their assessment scores. Results showed that higher highlighting frequency was related to higher factual comprehension scores but not higher inferential comprehension scores. In contrast, higher tagging frequency was related to higher inferential comprehension scores but not higher factual comprehension scores. In each case, the higher annotation frequency was only related to higher assessment scores when the subject created an above-average number of tags or highlights. The study suggests that different annotation methods are related to different comprehension outcomes.
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Keyword:
annotation; digital annotation; digital reading; Educational Technology; highlighting; smartphones; tagging
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URL: https://scholarworks.boisestate.edu/cgi/viewcontent.cgi?article=2925&context=td
https://scholarworks.boisestate.edu/td/1790
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Oscillatory activity and EEG phase synchrony of concurrent word segmentation and meaning-mapping in 9-year-old children
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In: ISSN: 1878-9293 ; EISSN: 1878-9307 ; Developmental Cognitive Neuroscience ; https://hal.archives-ouvertes.fr/hal-03334735 ; Developmental Cognitive Neuroscience, Elsevier, 2021, 51, pp.101010. ⟨10.1016/j.dcn.2021.101010⟩ (2021)
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From Error Annotation to Quantitative Analysis: Patterns in Russian Language Learning
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In: ISSN: 0036-0252 ; Russian language journal ; https://hal.archives-ouvertes.fr/hal-03376956 ; Russian language journal, American Councils for International Education, Michigan State University 2021, 71 (3), pp.39-70 (2021)
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Hierarchical-Task Reservoir for Online Semantic Analysis from Continuous Speech
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In: ISSN: 2162-237X ; IEEE Transactions on Neural Networks and Learning Systems ; https://hal.inria.fr/hal-03031413 ; IEEE Transactions on Neural Networks and Learning Systems, IEEE, 2021, ⟨10.1109/TNNLS.2021.3095140⟩ ; https://ieeexplore.ieee.org/abstract/document/9548713/metrics#metrics (2021)
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Corpus and Models for Lemmatisation and POS-tagging of Classical French Theatre
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In: EISSN: 2416-5999 ; Journal of Data Mining and Digital Humanities ; https://halshs.archives-ouvertes.fr/halshs-02591388 ; Journal of Data Mining and Digital Humanities, Episciences.org, 2021, ⟨10.46298/jdmdh.6485⟩ (2021)
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Corpus and Models for Lemmatisation and POS-tagging of Old French
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In: https://halshs.archives-ouvertes.fr/halshs-03353125 ; 2021 (2021)
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Old Catalan Morphosyntax: developing an annotated corpus
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In: EISSN: 2059-481X ; Journal of Open Humanities Data ; https://hal.archives-ouvertes.fr/hal-03617737 ; Journal of Open Humanities Data, Ubiquity Press, 2021, 7, pp.30. ⟨10.5334/johd.54⟩ (2021)
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Automated audio captioning by fine-tuning bart with audioset tags
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In: DCASE 2021 - 6th Workshop on Detection and Classification of Acoustic Scenes and Events ; https://hal.inria.fr/hal-03522488 ; DCASE 2021 - 6th Workshop on Detection and Classification of Acoustic Scenes and Events, Nov 2021, Virtual, Spain (2021)
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