Concepts identification of an NL query in NLIDB systems

This paper proposes a novel approach to capture the concept¹ of an NL query. Given an NL query, the query is mapped to a tagset, which carries the concepts information. The tagset was created by mapping every noun chunk to the attribute of a table (tableName.attributeNarne) and every verb chunk to a relation in the ER schema. The approach is discussed using the Courses Management domain of a University and can be extended to other domains. The tagset here was formed using the ER-schema of the Courses Management Portal of our university. We used the statistical approach to identify the concepts. We ourselves formed a tagged corpus with different types of NL queries. Conditional Random Field algorithm was used for the classification. The results are very promising and are compared to the rule based approach seen in Gupta et al. (2012) [1].