Minds and Machines

, Volume 7, Issue 1, pp 1–37

Strong Semantic Systematicity from Hebbian Connectionist Learning

  • Robert F. Hadley
  • Michael B. Hayward

DOI: 10.1023/A:1008252408222

Cite this article as:
Hadley, R.F. & Hayward, M.B. Minds and Machines (1997) 7: 1. doi:10.1023/A:1008252408222


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.


Copyright information

© Kluwer Academic Publishers 1997

Authors and Affiliations

  • Robert F. Hadley
    • 1
  • Michael B. Hayward
    • 2
  1. 1.School of Computing ScienceSimon Fraser UniversityBurnabyCanada
  2. 2.Department of Cognitive ScienceUniversity of CaliforniaSan Diego, La JollaUSA