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1
Eye Movement Traces of Linguistic Knowledge
In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021)
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2
Eye Movement Traces of Linguistic Knowledge ...
Berzak, Yevgeni. - : Open Science Framework, 2021
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3
Universal Dependencies 2.9
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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4
Universal Dependencies 2.8.1
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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5
Universal Dependencies 2.8
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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6
Eye Movement Traces of Linguistic Knowledge ...
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7
Assessing Language Proficiency from Eye Movements in Reading
In: Association for Computational Linguistics (2021)
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8
STARC: Structured Annotations for Reading Comprehension
In: Association for Computational Linguistics (2021)
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9
Bridging Information-Seeking Human Gaze and Machine Reading Comprehension
In: Association for Computational Linguistics (2021)
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10
Universal Dependencies 2.7
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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11
Universal Dependencies 2.6
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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12
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02425462 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2019, 45 (3), pp.559-601. ⟨10.1162/coli_a_00357⟩ ; https://www.mitpressjournals.org/doi/abs/10.1162/coli_a_00357 (2019)
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13
Universal Dependencies 2.5
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019
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14
Universal Dependencies 2.4
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019
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15
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ...
Ponti, Edoardo; O'Horan, Helen; Berzak, Yevgeni. - : Apollo - University of Cambridge Repository, 2019
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16
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
Reichart, Roi; Shutova, Ekaterina; Korhonen, Anna-Leena. - : MIT Press - Journals, 2019. : COMPUTATIONAL LINGUISTICS, 2019
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17
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
In: Computational Linguistics, Vol 45, Iss 3, Pp 559-601 (2019) (2019)
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18
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
In: https://hal.archives-ouvertes.fr/hal-01856176 ; 2018 (2018)
Abstract: Addressing the cross-lingual variation of grammatical structures and meaning categorization is a key challenge for multilingual Natural Language Processing. The lack of resources for the majority of the world's languages makes supervised learning not viable. Moreover, the performance of most algorithms is hampered by language-specific biases and the neglect of informative multilingual data. The discipline of Linguistic Typology provides a principled framework to compare languages systematically and empirically and documents their variation in publicly available databases. These enshrine crucial information to design language-independent algorithms and refine techniques devised to mitigate the above-mentioned issues, including cross-lingual transfer and multilingual joint models, with typological features. In this survey, we demonstrate that typology is beneficial to several NLP applications, involving both semantic and syntactic tasks. Moreover, we outline several techniques to extract features from databases or acquire them automatically: these features can be subsequently integrated into multilingual models to tie parameters together cross-lingually or gear a model towards a specific language. Finally, we advocate for a new typology that accounts for the patterns within individual examples rather than entire languages, and for graded categories rather than discrete ones, in oder to bridge the gap with the contextual and continuous nature of machine learning algorithms.
Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; [SCCO.COMP]Cognitive science/Computer science; [SCCO.LING]Cognitive science/Linguistics; [SCCO]Cognitive science; [SHS.INFO]Humanities and Social Sciences/Library and information sciences; [SHS.LANGUE]Humanities and Social Sciences/Linguistics; [SHS.STAT]Humanities and Social Sciences/Methods and statistics; Language typology; Machine learning; Natural Language Processing
URL: https://hal.archives-ouvertes.fr/hal-01856176
https://hal.archives-ouvertes.fr/hal-01856176/document
https://hal.archives-ouvertes.fr/hal-01856176/file/1807.00914.pdf
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19
Universal Dependencies 2.3
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018
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20
Second language learning from a multilingual perspective
Berzak, Yevgeni. - : Massachusetts Institute of Technology, 2018
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