1. Introduction
There has been considerable research in the area of document retrieval for over 30 years [1], dominated by the use of statistical methods to automatically match natural language user queries against data records. There has been interest in using natural language processing to enhance single term matching by adding phrases [3], yet to date natural language processing techniques have not significantly improved performance of document retrieval [2], although much effort has been expended in various attempts. The motivation and drive for using natural language processing (NLP) in docu-ment retrieval is mostly intuitive; users decide on the relevance of documents by reading and analyzing them. Thus, if a system can automate document analysis, this should help in the process of deciding on document relevance.