Mechanisms of implicit learning: connectionist models of sequence processing
Mechanisms of implicit learning: connectionist models of sequence processing
Supervised neural networks for the classification of structures
IEEE Transactions on Neural Networks
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
The graph neural network model
IEEE Transactions on Neural Networks
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Connectionist semantic modeling in natural language processing (a typical symbolic domain) is still a challenging problem. This paper introduces a novel technique, combining Discourse Representation Theory (DRT) with Recursive Neural Networks (RNN) in order to yield a neural model capable to discover properties and relationships among constituents of a knowledge-base expressed by natural language sentences. DRT transforms sequences of sentences into directed ordered acyclic graphs, while RNNs are trained to deal with such structured data. The acquired information allows the network to reply on questions, the answers of which are not directly expressed into the knowledge-base. A simple experimental demonstration, drawn from the context of a fairy tales is presented. Finally, on-going research direction are pointed-out.