One of the primary advantages of input-based modularization is that the individual knowledge sources (lexicon entries) can be grounded in the input they expect to be matched against. For instance, in Figure 3, the semantic input expected shows three variables, corresponding to the three case roles normally associated with a DIVIDE event. The process of linking these variables to the actual semantic structures for a particular input is known as binding. For the input shown in Figure 1, VAR2 will be bound to CONGLOMERATE-32 and VAR3 will be bound to CORPORATION-34.
Notice that, for this example, no AGENT exists for the DIVIDE-31 event, so that VAR1 will be left unbound. Binding constraints will simply eliminate any syntactic choices that contain non-optional unbound variables. In this case, it will rule out the first syntactic realization for DIVIDE shown in Figure 3.
The grounding of the input afforded by the binding process also allows us to simplify the other types of constraints described below. Each of these types of constraints, automatically processed in our system, in task-based systems typically require complex rules to be acquired manually.