Alexander G. Hauptmann
The development of larger scale natural language systems has been hampered by the need to manually create mappings from syntactic structures into meaning representations. A new approach to semantic interpretation is proposed, which uses partial syntactic structures as the main unit of abstraction for interpretation rules. This approach can work for a variety of syntactic representations corresponding to directed acyclic graphs. It is designed to map into meaning representations based on frame hierarchies with inheritance. We define semantic interpretation rules in a compact format. The format is suitable for automatic rule extension or rule generalization, when existing hand-coded rules do not cover the current input. Furthermore, automatic discovery of semantic interpretation rules from input/output examples is made possible by this new rule format. The principles of the approach are validated in a comparison to other methods on a separately developed domain. Instead of relying purely on painstaking human effort, this paper combines human expertise with computer learning strategies to successfully overcome the bottleneck of semantic interpretation.