The DOI matches its Crossref record; 138 of the 138 references it cites were found in a registry or an index.
Representing word meaning and order information in a composite holographic lexicon.
Abstract
The authors present a computational model that builds a holographic lexicon representing both word meaning and word order from unsupervised experience with natural language. The model uses simple convolution and superposition mechanisms (cf. B. B. Murdock, 1982) to learn distributed holographic representations for words. The structure of the resulting lexicon can account for empirical data from classic experiments studying semantic typicality, categorization, priming, and semantic constraint in sentence completions. Furthermore, order information can be retrieved from the holographic representations, allowing the model to account for limited word transitions without the need for built-in transition rules. The model demonstrates that a broad range of psychological data can be accounted for directly from the structure of lexical representations learned in this way, without the need for complexity to be built into either the processing mechanisms or the representations. The holographic representations are an appropriate knowledge representation to be used by higher order models of language comprehension, relieving the complexity required at the higher level.
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Cited by
Topics in semantic representation
Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007) · Psychological Review · doi:10.1037/0033-295x.114.2.211 · Cited by 1,122
Composition in Distributional Models of Semantics
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Chapter 2 Design for a Working Memory
Oberauer, K. (2009) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(09)51002-x · Cited by 636
Distributional Memory: A General Framework for Corpus-Based Semantics
Baroni, M., & Lenci, A. (2010) · Computational Linguistics · doi:10.1162/coli_a_00016 · Cited by 659
A structured vector space model for word meaning in context
Erk, K., & Padó, S. (2008) · Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08 · doi:10.3115/1613715.1613831 · Cited by 366
Metadata
- Authors
- Jones, M. N., & Mewhort, D. J. K.
- Venue
- Psychological Review
- Published
- 2007
- Volume
- 114
- Issue
- 1
- Pages
- 1-37
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W1986707196
- Cited by
- 613
- References
- 138
- FWCI
- 15.16
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
