A Probabilistic Semantics for Natural Language

Probabilistic and stochastic methods have been fruitfully applied to a wide variety of problems in grammar induction, natural language processing, and cognitive modeling. In this paper we explore the possibility of developing a class of combinatorial semantic representations for natural languages that compute the semantic value of a (declarative) sentence as a probability value which expresses the likelihood of competent speakers of the language accepting the sentence as true in a given model, relative to a specification of the world. Such an approach to semantic representation treats the pervasive gradience of semantic properties as intrinsic to speakers' linguistic knowledge, rather than the result of the interference of performance factors in processing and interpretation. In order for this research program to succeed, it must solve three central problems. First, it needs to formulate a type system that computes the probability value of a sentence from the semantic values of its syntactic constituents. Second, it must incorporate a viable probabilistic logic into the representation of semantic knowledge in order to model meaning entailment. Finally, it must show how the specified class of semantic representations can be efficiently learned. We construct a probabilistic semantic fragment and consider how the approach that the fragment instantiates addresses each of these three issues.

This is joint work with Jan van Eijck, CWI, Amsterdam.