Building Hybrid Knowledge Representations from Text

A significant obstacle to the development of intelligent natural language processing systems is the lack of rich knowledge bases containing representations of world knowledge. For experimental systems it is common practice to construct small knowledge bases by hand; however, this approach does not scale well to large systems. An alternative is to attempt to extract the desired information from existing knowledge sources intended for human consumption; however, attempts to construct broad-coverage knowledge bases using in-depth analysis have met with limited success. In this paper we present some work on an alternative approach that involves using shallow processing techniques to build a hybrid knowledge representation that stores information in a partially analysed form.