Towards a theory of natural language interfaces to databases
Proceedings of the 8th international conference on Intelligent user interfaces
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Fast methods for kernel-based text analysis
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Using LTAG based features in parse reranking
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Boosting-based parse reranking with subtree features
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Using string-kernels for learning semantic parsers
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Semantic role labeling via FrameNet, VerbNet and PropBank
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Speeding up training with tree kernels for node relation labeling
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Exploring syntactic features for relation extraction using a convolution tree kernel
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Fast and effective kernels for relational learning from texts
Proceedings of the 24th international conference on Machine learning
Tree kernels for semantic role labeling
Computational Linguistics
Towards Building Robust Natural Language Interfaces to Databases
NLDB '08 Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems
Kernel methods, syntax and semantics for relational text categorization
Proceedings of the 17th ACM conference on Information and knowledge management
Semantic role labeling via tree kernel joint inference
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Engineering of syntactic features for shallow semantic parsing
FeatureEng '05 Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing
Syntactic Structural Kernels for Natural Language Interfaces to Databases
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Semantic model for improving the performance of natural language interfaces to databases
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Resolving syntactic ambiguities in natural language specification of constraints
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Learning dependency-based compositional semantics
Computational Linguistics
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Automatically mapping natural language semantics into programming languages has always been a major and interesting challenge in Computer Science. In this paper, we approach such problem by carrying out mapping at syntactic level and then applying machine learning algorithms to derive an automatic translator of natural language questions into their associated SQL queries. To build the required training and test sets, we designed an algorithm, which, given an initial corpus of questions and their answers, semi-automatically generates the set of possible incorrect and correct pairs. We encode such relational pairs in Support Vector Machines by means of kernel functions applied to the syntactic trees of questions and queries. The accurate results on automatic classification of the above pairs above, suggest that our approach captures the shared semantics between the two languages.