Supporting rule-based representations with corpus-derived lexical information

Abstract

The pervasive ambiguity of language allows sentences that differ in just one lexical item to have rather different inference patterns. This would be no problem if the different lexical items fell into clearly definable and easy to represent classes. But this is not the case. To draw the correct inferences we need to look how the referents of the lexical items in the sentence (or broader context) interact in the described situation. Given that the knowledge our systems have of the represented situation will typically be incomplete, the classifications we come up with can only be probabilistic. We illustrate this problem with an investigation of various inference patterns associated with predications of the form Verb from X to Y, especially go from X to Y. We characterize the various readings and make an initial proposal about how to create the lexical classes that will allow us to draw the correct inferences in the different cases.