From Syntax to Meaning in Natural Language Processing

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.


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