Linguistic processing of text for a large-scale conceptual Information Retrieval system

This paper describes our large-scale effort to build a conceptual Information Retrieval system that converts a large volume of natural language text into Conceptual Graph representation by means of knowledge-based processing. In order to automatically extract concepts and conceptual relations between concepts from texts, we constructed a knowledge base consisting of over 12,000 case frames for verbs and a large number of other linguistic patterns that reveal conceptual relations. They were used to process a Wall Street Journal database covering a period of three years. We describe our methods for constructing the knowledge base, how the linguistic knowledge is used to process the text, and how the retrieval system makes use of the rich representation of documents and information needs.