Abstract
The linguistic description and simulation of semantic and syntactic structures suggested by different theoretical accounts are discussed. We consider thereby established programs including natural/conceptual semantics, cognitive grammar, generative syntax, dependency grammar, and connectionist accounts. It is argued that the distinction between semantic and conceptual representations is a useful approach for empirical research on meanings. In the attempt to simulate sentence processing, neural network models have a certain degree of predictive power. Reverse engineering programs, which built virtual brain libraries, might be a very useful mean to improve our understanding of the neurobiological mechanisms.
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Notes
- 1.
- 2.
For simplification, we do not include here grammatical features such as agreement, tense or type of NP.
- 3.
A transfer function can be for instance sigmoidal or hard limited. The sigmoid function takes a net value and generates an output between 0 and 1 and a hard limited function sets for example a fixed range such as < 0.5 = 0; ≥ 0.5 = 1.
- 4.
It should be emphasize that connectionist models include distributed (non-modular) but also sequential (modular) computations, in which different types of data are represented over different groups of units allowing the direct interaction of only specific datasets as in simple recurrent networks.
- 5.
Part of the development of a CA is neural or synaptic death and growth.
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Hillert, D. (2014). Semantics and Syntax. In: The Nature of Language. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0609-3_6
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