Fodor's and Pylyshyn's stand on systematicity in thought and language has been debated and criticized. Van Gelder and Niklasson, among others, have argued that Fodor and Pylyshyn offer no precise definition of systematicity. However, our concern here is with a learning based formulation of that concept. In particular, Hadley has proposed that a network exhibits strong semantic systematicity when, as a result of training, it can assign appropriate meaning representations to novel sentences (both simple and embedded) which contain words in (...) syntactic positions they did not occupy during training. The experience of researchers indicates that strong systematicity in any form is difficult to achieve in connectionist systems.Herein we describe a network which displays strong semantic systematicity in response to Hebbian, connectionist training. During training, two-thirds of all nouns are presented only in a single syntactic position (either as grammatical subject or object). Yet, during testing, the network correctly interprets thousands of sentences containing those nouns in novel positions. In addition, the network generalizes to novel levels of embedding. Successful training requires a, corpus of about 1000 sentences, and network training is quite rapid. The architecture and learning algorithms are purely connectionist, but classical insights are discernible in one respect, viz, that complex semantic representations spatially contain their semantic constituents. However, in other important respects, the architecture is distinctly non-classical. (shrink)

This paper investigates the mechanisms a program may use to learn conceptual structures that represent natural language meaning. A computer program named Moran is described that infers conceptual structures from pictorial input data. Moran is presented with “snapshots” of an environment and an English sentence describing the action that takes place between the snapshots. The learning task is to associate each root verb with a conceptual structure that represents the types of objects that participate in the action and the changes (...) the objects undergo during the action. Four learning mechanisms are shown to be adequate to accomplish this learning task. The learning mechanisms are described along with the conditions under which each is invoked and the effect each has on existing memory structures. The conceptual structure Moran inferred for one root verb is shown. (shrink)