Augmenting WordNet for deep understanding of text

Abstract

One of the big challenges in understanding text, i.e., constructing an overall coherent representation of the text, is that much information needed in that representation is unstated (implicit). Thus, in order to "fill in the gaps" and create an overall representation, language processing systems need a large amount of world knowledge, and creating those knowledge resources remains a fundamental challenge. In our current work, we are seeking to augment WordNet as a knowledge resource for language understanding in several ways: adding in formal versions of its word sense definitions (glosses); classifying the morphosemantic links between nouns and verbs; encoding a small number of "core theories" about WordNet's most commonly used terms; and adding in simple representations of scripts. Although this is still work in progress, we describe our experiences so far with what we hope will be a significantly improved resource for the deep understanding of language.