Syntactic Structure from Deep Learning
Article Properties
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LanguageEnglish
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Publication Date2021/01/14
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Journal
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Indian UGC (journal)
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Refrences78
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Citations45
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Tal Linzen Department of Linguistics and Center for Data Science, New York University, New York, NY 10003, USA;
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Marco Baroni Facebook AI Research, Paris 75002, France;Catalan Institute for Research and Advanced Studies, Barcelona 08010, SpainDepartament de Traducció i Ciències del Llenguatge, Universitat Pompeu Fabra, Barcelona 08018, Spain
Abstract
Cite
Linzen, Tal, and Marco Baroni. “Syntactic Structure from Deep Learning”. Annual Review of Linguistics, vol. 7, no. 1, 2021, pp. 195-12, https://doi.org/10.1146/annurev-linguistics-032020-051035.
Linzen, T., & Baroni, M. (2021). Syntactic Structure from Deep Learning. Annual Review of Linguistics, 7(1), 195-212. https://doi.org/10.1146/annurev-linguistics-032020-051035
Linzen T, Baroni M. Syntactic Structure from Deep Learning. Annual Review of Linguistics. 2021;7(1):195-212.
Journal Categories
Language and Literature
Philology
Linguistics
Social Sciences
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Using Deep Neural Networks to Learn Syntactic Agreement | Linguistic Issues in Language Technology | 4 | 2017 | |
| Using Deep Neural Networks to Learn Syntactic Agreement | 2020 | |||
| Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies | 2019 | |||
| NIPS'14: Proceedings of the 27th International Conference on Neural Information Processing Systems | 2014 | |||
| In Handbook of Mathematical Psychology | 1963 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Nativism and empiricism in artificial intelligence | Philosophical Studies |
| 2024 | |
| Confidence-based Syntax encoding network for better ancient Chinese understanding | Information Processing & Management |
| 2024 | |
| Large-scale benchmark yields no evidence that language model surprisal explains syntactic disambiguation difficulty | Journal of Memory and Language |
| 2024 | |
| Gender classification of Korean personal names: Deep neural networks versus human judgments | Lingua |
| 2024 | |
| Dissociating language and thought in large language models | Trends in Cognitive Sciences |
| 7 | 2024 |
Citations Analysis
The category
Philosophy. Psychology. Religion: Psychology 15
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models and was published in 2020. The most recent citation comes from a 2024 study titled Neural Generative Models and the Parallel Architecture of Language: A Critical Review and Outlook. This article reached its peak citation in 2022, with 15 citations. It has been cited in 32 different journals, 31% of which are open access. Among related journals, the Cognitive Science cited this research the most, with 5 citations. The chart below illustrates the annual citation trends for this article.