Semantic Mapping between Natural Language Questions and SQL Queries via Syntactic Pairing

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.