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One model for the learning of language.
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In: Proceedings of the National Academy of Sciences of the United States of America, vol 119, iss 5 (2022)
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Computational Measures of Deceptive Language: Prospects and Issues
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In: ISSN: 2297-900X ; EISSN: 2297-900X ; Frontiers in Communication ; https://hal.archives-ouvertes.fr/hal-03629780 ; Frontiers in Communication, Frontiers, 2022, 7, pp.792378. ⟨10.3389/fcomm.2022.792378⟩ (2022)
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Animal linguistics in the making: the Urgency Principle and titi monkeys’ alarm system
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In: ISSN: 0394-9370 ; Ethology Ecology and Evolution ; https://hal.inrae.fr/hal-03518874 ; Ethology Ecology and Evolution, Taylor & Francis, 2022, pp.1-17. ⟨10.1080/03949370.2021.2015452⟩ (2022)
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A Dataset for Toponym Resolution in Nineteenth-Century English Newspapers
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In: Journal of Open Humanities Data; Vol 8 (2022); 3 ; 2059-481X (2022)
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Using Machine Learning for Pharmacovigilance: A Systematic Review
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In: Pharmaceutics; Volume 14; Issue 2; Pages: 266 (2022)
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Vec2Dynamics: A Temporal Word Embedding Approach to Exploring the Dynamics of Scientific Keywords—Machine Learning as a Case Study
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In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 21 (2022)
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Abstract:
The study of the dynamics or the progress of science has been widely explored with descriptive and statistical analyses. Also this study has attracted several computational approaches that are labelled together as the Computational History of Science, especially with the rise of data science and the development of increasingly powerful computers. Among these approaches, some works have studied dynamism in scientific literature by employing text analysis techniques that rely on topic models to study the dynamics of research topics. Unlike topic models that do not delve deeper into the content of scientific publications, for the first time, this paper uses temporal word embeddings to automatically track the dynamics of scientific keywords over time. To this end, we propose Vec2Dynamics, a neural-based computational history approach that reports stability of k-nearest neighbors of scientific keywords over time; the stability indicates whether the keywords are taking new neighborhood due to evolution of scientific literature. To evaluate how Vec2Dynamics models such relationships in the domain of Machine Learning (ML), we constructed scientific corpora from the papers published in the Neural Information Processing Systems (NIPS; actually abbreviated NeurIPS) conference between 1987 and 2016. The descriptive analysis that we performed in this paper verify the efficacy of our proposed approach. In fact, we found a generally strong consistency between the obtained results and the Machine Learning timeline.
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Keyword:
computational linguistics; k -NN stability; machine learning; scientific literature; temporal word embedding
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URL: https://doi.org/10.3390/bdcc6010021
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Hebrew Transformed: Machine Translation of Hebrew Using the Transformer Architecture
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Causal and Semantic Relations in L2 Text Processing: An Eye-Tracking Study
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Nahatame, Shingo. - : University of Hawaii National Foreign Language Resource Center, 2022. : Center for Language & Technology, 2022
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Phylogenetic trees: Grammar versus vocabulary
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In: Russian Journal of Linguistics, Vol 26, Iss 1, Pp 31-50 (2022) (2022)
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French de and en as expressions of the genitive case: a unified analysis within LFG and computational implementation in XLE1
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In: DELTA: Documentação e Estudos em Linguística Teórica e Aplicada; v. 37 n. 1 (2021) ; 1678-460X ; 0102-4450 (2022)
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Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics ; Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics: Dagstuhl Seminar 21351
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In: Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03507948 ; Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics, Aug 2021, pp.89--138, 2021, 2192-5283. ⟨10.4230/DagRep.11.7.89⟩ ; https://gitlab.com/unlid/dagstuhl-seminar/-/wikis/home (2021)
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Type-logical investigations: proof-theoretic, computational and linguistic aspects of modern type-logical grammars
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In: https://hal-lirmm.ccsd.cnrs.fr/tel-03452731 ; Computation and Language [cs.CL]. Université Montpellier, 2021 (2021)
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Arc-Eager Construction Provides Learning Advantage Beyond Stack Management
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Dialogue Modeling in a Dynamic Framework ; Modélisation dynamique des dialogues
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In: https://hal.archives-ouvertes.fr/tel-03541628 ; Computation and Language [cs.CL]. Université de Lorraine; École doctorale IAEM Lorraine - Informatique, Automatique, Électronique - Électrotechnique, Mathématiques de Lorraine, 2021. English. ⟨NNT : 2021LORR0199⟩ (2021)
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SM to: Is there a bilingual disadvantage for word segmentation? A computational modeling approach ...
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Micropoetry meets Neurocognitive Poetics ... : Influence of Associations on the Reception of Poetry ...
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