ABSTRACT
If we are to develop language processing systems that model human capabilities and performance, we must identify correspondences between the grammatical features and meaning of language and employ them in our computational models of sentence interpretation. In this paper, we present a computational model of sentence interpretation and a theory of compositional semantics. Our model provides a method for addressing a range of lexical novelty (e.g., novel verbs, novel uses of known verbs), relying on a semantic representation that maintains principled correspondences with syntactic form. In our approach, syntactic structure preserves critical information about the hierarchical structure of semantic interpretations. This property of the semantic representation along with restrictions on semantic interpretations enable the model to infer the semantics of novel verbs, disambiguate the semantics of known verbs, and determine the contributions that verb arguments make to sentence interpretation in a constrained and principled manner. This research offers a fruitful approach for using linguistic analysis to address the recovery of meaning in natural language processing systems.
