Natural language learning by computer
Natural language learning by computer
The hierarchical organization of predicate frames for interpretive mapping in natural language processing
Problems in natural-language interface to DBMS with examples from EUFID
ANLC '83 Proceedings of the first conference on Applied natural language processing
Semantic interpretation using KL-ONE
ACL '84 Proceedings of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics
An integrated framework for semantic and pragmatic interpretation
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
Taxonomy, descriptions, and individuals in natural language understanding
ACL '79 Proceedings of the 17th annual meeting on Association for Computational Linguistics
A theory of language acquisition based on general learning principles
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
Grammatical relations as the basis for natural language parsing and text understanding
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
Human engineering fcr applied natural language processing
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 1
The universal parser architecture for knowledge-based machine translation
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
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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.