Sentence examples for model of sentence from inspiring English sources

In this article we present a neural network model of sentence generation.

To address these issues, a connectionist model of sentence production was developed.

In this paper, we propose two information-theoretic measures-ambiguity and distinctiveness-derived from a simple model of sentence processing.

We present a connectionist model of sentence comprehension that does not implement a symbol system yet behaves systematically.

Exploiting the similarity of constraint satisfaction and DNA self-assembly, we explore a DNA assembly model of sentence generation.

Results are discussed in the context of a construction-based model of sentence processing (see Dominey and Hoen, 2006, this issue) that is based on a dual-path processing mechanism separating function and content information processing.

Thus, they explore whether a predictive model of sentencing outcome can be constructed from measures of degree centrality, betweenness centrality, and closeness centrality together with graph density and centralization metrics.

As potential candidates for explaining human cognition, connectionist models of sentence processing must demonstrate their ability to behave systematically, generalizing from a small training set.

In this article we take 'symbolic models of sentence processing' to mean approaches in which processing is characterized in terms of relatively discrete, combinatorial representations.

The current research attempts to reconcile these data with several current neurophysiologically based models of sentence processing, through the specification of a neural network model whose architecture is constrained by the known cortico-striato-thalamo-cortical (CSTC) neuroanatomy of the human language system.

Let us write for the set of models of sentence φ.