Natural language understanding for soft information fusion

Tractor is a system for understanding English messages within the context of hard and soft information fusion for situation assessment. Tractor processes a message through syntactic processors, and represents the result in a formal knowledge representation language. The result is a hybrid syntactic-semantic knowledge base that is mostly syntactic. Tractor then adds relevant ontological and geographic information. Finally, it applies hand-crafted syntax-semantics mapping rules to convert the syntactic information into semantic information, although the final result is still a hybrid syntactic-semantic knowledge base. This paper presents the various stages of Tractor's natural language understanding process, with particular emphasis on discussions of the representation used and of the syntax-semantics mapping rules.