Distributed representations, simple recurrent networks, and grammatical structure
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Elman's model learned to predict the verb form correctly despite an intervening clause, showing it acquired sensitivity to syntactic structure, not just local co-occurrence statistics.
“The model learned to predict the verb form correctly despite the intervening clause, showing that it acquired sensitivity to the syntactic structure of language, not just local co-occurrence statistics.”
From Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models (2020) · cites this paper as [16]
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- Authors
- Elman, J. L.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Elman, Jeffrey L.
- ORCID
- Elman, Jeffrey L.
- Venue
- Machine Learning
- Published
- 1991-09
- Volume
- 7
- Issue
- 2-3
- Pages
- 195-225
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2087946919
- Cited by
- 1,354
- References
- 76
- Citations by year
- 2026: 4 · 2025: 17 · 2024: 50 · 2023: 42 · 2022: 74 · 2021: 48 · 2020: 63 · 2019: 65 · 2018: 46 · 2017: 46 · 2016: 41 · 2015: 42 · 2014: 34 · 2013: 44 · 2012: 38
- FWCI
- 53.08
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- Funders
- University of California, San Diego
- Office of Naval Research (N00014)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Keywords
- distributed representations, Elman neural network, grammatical structure, phrase structure, relative clauses, linguistic representation
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