Sparse single-hidden layer feedforward network for mapping natural language questions to sql queries

Abstract

Mapping natural language (NL) statements into SQL queries allows users to interact with systems through everyday language. Semantic parsing has seen a growing interest over the past decades. In this paper, we extend single hidden layer feedforward network (SLFN) by adding the Kullback-Liebler (KL) divergence parameter to its objective function. We refer to this algorithm as Sparse SLFN (S-SLFN) which can learn whether an SQL query answers a particular NL question. With Bag of Words (BoW) representing the questions and the queries, the algorithm, by enforcing sparsity, is meant to retain robust features representing informative relationships and structure of the data. Experimental results show that S-SLFN outperforms SLFN and other algorithms for the GeoQueries dataset by a respectable margin. © 2014 Springer International Publishing Switzerland.