Abstract
A core problem in data mining is to retrieve data in a easy and human friendly way. Automatically translating natural language questions into SQL queries would allow for the design of effective and useful database systems from a user viewpoint. Interesting previous work has been focused on the use of machine learning algorithms for automatically mapping natural language (NL) questions to SQL queries.
In this paper, we present many structural kernels and their combinations for inducing the relational semantics between pairs of NL questions and SQL queries. We measure the effectiveness of such kernels by using them in Support Vector Machines to select the queries that correctly answer to NL questions. Experimental results on two different datasets show that our approach is viable and that syntactic information under the form of pairs of syntactic tree fragments (from queries and questions) plays a major role in deriving the relational semantics between the two languages.
Chapter PDF
Similar content being viewed by others
References
Kate, R.J., Mooney, R.J.: Using string-kernels for learning semantic parsers. In: Proceedings of the 21st ICCL and 44th Annual Meeting of the ACL, Sydney, Australia, July 2006, pp. 913–920. Association for Computational Linguistics (2006)
Popescu, A.M., Etzioni, A.O., Kautz, A.H.: Towards a theory of natural language interfaces to databases. In: Proceedings of the 2003 International Conference on Intelligent User Interfaces, Miami, pp. 149–157. Association for Computational Linguistics (2003)
Minock, M., Olofsson, P., Näslund, A.: Towards building robust natural language interfaces to databases. In: Kapetanios, E., Sugumaran, V., Spiliopoulou, M. (eds.) NLDB 2008. LNCS, vol. 5039, pp. 187–198. Springer, Heidelberg (2008)
Zettlemoyer, L.S., Collins, M.: Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars. In: UAI, pp. 658–666 (2005)
Wong, Y.W., Mooney, R.: Learning for semantic parsing with statistical machine translation. In: Proceedings of the Human Language Technology Conference of the NAACL, Main Conference, New York City, USA, June 2006, pp. 439–446. Association for Computational Linguistics (2006)
Dale, R., Somers, H.L., Moisl, H. (eds.): 9. In: Database Interfaces, pp. 209–240. Marcel Dekker Inc., New York (2000)
Tang, L.R., Mooney, R.J.: Using multiple clause constructors in inductive logic programming for semantic parsing. In: Proceedings of the 12th European Conference on Machine Learning, Freiburg, Germany, pp. 466–477 (2001)
Ge, R., Mooney, R.: A statistical semantic parser that integrates syntax and semantics. In: Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005), Ann Arbor, Michigan, June 2005, pp. 9–16. Association for Computational Linguistics (2005)
Winograd, T.: Understanding Natural Language. Academic Press, New York (1972)
Lodhi, H., Taylor, J.S., Cristianini, N., Watkins, C.J.C.H.: Text classification using string kernels. In: NIPS, pp. 563–569 (2000)
Collins, M., Duffy, N.: New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron. In: Proceedings of ACL 2002 (2002)
Vishwanathan, S.V.N., Smola, A.J.: Fast kernels for string and tree matching. In: Advances in Neural Information Processing Systems, vol. 15, pp. 569–576. MIT Press, Cambridge (2003)
Moschitti, A.: Efficient convolution kernels for dependency and constituent syntactic trees. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 318–329. Springer, Heidelberg (2006)
Giordani, A., Moschitti, A.: Semantic mapping between natural language questions and sql queries via syntactic pairing. In: NLDB 2009: Proceedings of the 13th international conference on Natural Language and Information Systems (2009)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Zhang, D., Lee, W.S.: Question classification using support vector machines. In: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pp. 26–32. ACM Press, New York (2003)
Salton, G.: Recent trends in automatic information retrieval. In: SIGIR 1986, Proceedings of the 9th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Pisa, Italy, September 8-10, 1986, pp. 1–10. ACM, New York (1986)
Joachims, T.: Making large-scale SVM learning practical. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods (1999)
Moschitti, A., Quarteroni, S., Basili, R., Manandhar, S.: Exploiting syntactic and shallow semantic kernels for question/answer classification. In: Proceedings of ACL 2007, Prague, Czech Republic (2007)
Moschitti, A., Quarteroni, S.: Kernels on linguistic structures for answer extraction. In: Proceedings of ACL 2008: HLT, Short Papers, Columbus, Ohio (2008)
Chali, Y., Joty, S.: Improving the performance of the random walk model for answering complex questions. In: Proceedings of ACL 2008: HLT, Short Papers, Columbus, Ohio, pp. 9–12 (2008)
Shen, D., Lapata, M.: Using semantic roles to improve question answering. In: Proceedings of EMNLP-CoNLL (2007)
Surdeanu, M., Ciaramita, M., Zaragoza, H.: Learning to rank answers on large online QA collections. In: Proceedings of ACL 2008: HLT, Columbus, Ohio (2008)
Basili, R., Moschitti, A., Pazienza, M.: A text classifier based on linguistic processing. In: Proceedings of IJCAI 1999, Machine Learning for Information Filtering (1999)
Charniak, E.: A maximum-entropy-inspired parser. In: Proceedings of NAACL 2000 (2000)
Cancedda, N., Gaussier, E., Goutte, C., Renders, J.M.: Word sequence kernels. J. Mach. Learn. Res. 3, 1059–1082 (2003)
Moschitti, A.: Kernel methods, syntax and semantics for relational text categorization. In: Proceeding of CIKM 2008, NY, USA (2008)
Moschitti, A., Bejan, C.: A semantic kernel for predicate argument classification. In: Proceedings of CoNLL 2004, Boston, MA, USA (2004)
Moschitti, A., Coppola, B., Pighin, D., Basili, R.: Engineering of syntactic features for shallow semantic parsing. In: Proceedings of ACL 2005 Workshop on Feature Engineering for Machine Learning in NLP, USA (2005)
Moschitti, A., Pighin, D., Basili, R.: Tree kernels for semantic role labeling. Computational Linguistics 34(2), 193–224 (2008)
Moschitti, A., Zanzotto, F.: Fast and effective kernels for relational learning from texts. In: Ghahramani, Z. (ed.) Proceedings of the 24th Annual International Conference on Machine Learning, ICML 2007 (2007)
Moschitti, A., Pighin, D., Basili, R.: Semantic role labeling via tree kernel joint inference. In: Proceedings of CoNLL-X, New York City (2006)
Chandra, Y., Mihalcea, R.: Natural language interfaces to databases, University of North Texas, Thesis, M.S. (2006)
Kudo, T., Matsumoto, Y.: Fast Methods for Kernel-Based Text Analysis. In: Hinrichs, E., Roth, D. (eds.) Proceedings of ACL, pp. 24–31 (2003)
Cumby, C., Roth, D.: Kernel Methods for Relational Learning. In: Proceedings of ICML 2003, Washington, DC, USA, pp. 107–114 (2003)
Culotta, A., Sorensen, J.: Dependency Tree Kernels for Relation Extraction. In: ACL 2004, Barcelona, Spain, pp. 423–429 (2004)
Kudo, T., Suzuki, J., Isozaki, H.: Boosting-based parse reranking with subtree features. In: Proceedings of ACL 2005, US (2005)
Toutanova, K., Markova, P., Manning, C.: The Leaf Path Projection View of Parse Trees: Exploring String Kernels for HPSG Parse Selection. In: Proceedings of EMNLP 2004, Barcelona, Spain (2004)
Kazama, J., Torisawa, K.: Speeding up Training with Tree Kernels for Node Relation Labeling. In: Proceedings of EMNLP 2005, Toronto, Canada, pp. 137–144 (2005)
Shen, L., Sarkar, A., Joshi, A.k.: Using LTAG Based Features in Parse Reranking. In: EMNLP, Sapporo, Japan (2003)
Zhang, M., Zhang, J., Su, J.: Exploring Syntactic Features for Relation Extraction using a Convolution tree kernel. In: Proceedings of NAACL, New York City, USA, pp. 288–295 (2006)
Zhang, D., Lee, W.: Question classification using support vector machines. In: Proceedings of SIGIR 2003, Toronto, Canada. ACM Press, New York (2003)
Giuglea, A.M., Moschitti, A.: Semantic role labeling via framenet, verbnet and propbank. In: Proceedings of ACL 2006, Sydney, Australia (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Giordani, A., Moschitti, A. (2009). Syntactic Structural Kernels for Natural Language Interfaces to Databases. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2009. Lecture Notes in Computer Science(), vol 5781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04180-8_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-04180-8_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04179-2
Online ISBN: 978-3-642-04180-8
eBook Packages: Computer ScienceComputer Science (R0)