Efficient top-down induction of logic programs
ACM SIGART Bulletin
Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
Machine Learning
Relational learning of pattern-match rules for information extraction
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Towards a theory of natural language interfaces to databases
Proceedings of the 8th international conference on Intelligent user interfaces
Learning Logical Definitions from Relations
Machine Learning
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Bagging and boosting a treebank parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A statistical semantic parser that integrates syntax and semantics
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Learning to parse database queries using inductive logic programming
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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Building or learning semantic parsers has been an interesting approach for creating natural language interfaces (NLI's) for databases. Recently, the problem of imperfect precision in an NLI has been brought up as an NLI that might answer a question incorrectly can render it unstable, if not useless. In this paper, an approach based on ensemble learning is proposed to trivially address the problem of unreliability in an NLI due to imperfect precision in the semantic parser in a way that also allows the recall of the NLI to be improved. Experimental results in two real world domains suggested that such an approach can be promising.