Traditional symbolic approaches to language have stressed the importance of constituency. Syntactic generalizations, such as grammar entries and universal principles, are expressed exclusively in terms of syntactic constituents, such as noun phrases and sentences. On the other hand, connectionist approaches to language have stressed the importance of context. This connectionist architecture captures generalizations over individual constituents, in addition to generalizations over contexts. However, simple TSVB networks do not capture generalizations over pairs of constituents, and thus cannot effectively use relationships between constituents.
Symbolic approaches do use relationships between constituents.
Thus the appropriateness of simple TSVB networks as a computational architecture for syntactic parsing depends on whether the use of context is sufficient to replace the use of relationships between constituents. In each of the cases where symbolic approaches to syntactic parsing need to use relationships between constituents, the effects of these relationships can be implemented in terms of properties of individual constituents plus properties of the sentence as a whole. This is done by using the contextual properties to communicate information about one constituent to the other constituents it is in the relation with. The difference between this connectionist solution and the symbolic solution using explicit relations is that the connectionist solution can only apply to one instance of a relationship at a time, while the symbolic solution generalizes to multiple instances. In particular, the long distance dependencies between wh- fillers and their gaps can only be processed one at a time.