Abstract. As potential candidates for human cognition, connection- ist models of sentence processing must learn to behave systematically by.

We study this capacity for the recently introduced recursive self-organizing neural network model and show that its performance is comparable with ESNs.

This work studies the capacity for recursive self-organizing neural network model to generalize in artificial language learning tasks and shows that its ...

We investigate this capacity for a recently introduced model that consists of separately trained modules: a recursive self-organizing module for learning ...

Systematicity in sentence processing with a recursive self-organizing neural network · I. FarkašM. Crocker. Computer Science. The European Symposium on ...

We investigate this capacity for a recently introduced model that consists of separately trained modules: a recursive self-organizing module for learning ...

Oct 25, 2007 · Crocker, Systematicity in sentence processing with a recursive self- organizing neural network, in: Proceedings of the 15th European Symposium.

We study this capacity for the recently introduced recursive self-organizing neural network model and show that its performance is comparable with ESNs.

We used data sets containing recursive linguistic structures and trained the Elman simple recurrent network (SRN) for the next-symbol prediction task.

... Systematicity in sentence processing with a recursive self-organizing neural network}}, booktitle={Proceedings of the 15th European Symposium on Artificial ...