Abstract
The are two main approaches to the representation of meaning in Computational Linguistics: a symbolic approach and a distributional approach. This paper considers the fundamental question of how these approaches might be combined. The proposal is to adapt a method from the Cognitive Science literature, in which symbolic and connectionist representations are combined using tensor products. Possible applications of this method for language processing are described. Finally, a potentially fruitful link between Quantum Mechanics, Computational Linguistics, and other related areas such as Information Retrieval and Machine Learning, is proposed.
Keywords
ConnectionismComputer scienceMeaning (existential)Representation (politics)Computational linguisticsArtificial intelligenceThe SymbolicCognitionCognitive scienceNatural language processingTheoretical computer scienceArtificial neural networkEpistemologyPsychology
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Publication Info
- Year
- 2007
- Type
- article
- Pages
- 52-55
- Citations
- 117
- Access
- Closed
External Links
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Cite This
Stephen Clark,
Stephen Pulman
(2007).
Combining Symbolic and Distributional Models of Meaning.
Oxford University Research Archive (ORA) (University of Oxford)
, 52-55.