  Translating Natural Language Queries (NLQs) to Structured Query Language
(SQL) in interfaces deployed in relational databases is a challenging task,
which has been widely studied in database community recently. Conventional rule
based systems utilize series of solutions as a pipeline to deal with each step
of this task, namely stop word filtering, tokenization, stemming/lemmatization,
parsing, tagging, and translation. Recent works have mostly focused on the
translation step overlooking the earlier steps by using ad-hoc solutions. In
the pipeline, one of the most critical and challenging problems is keyword
mapping; constructing a mapping between tokens in the query and relational
database elements (tables, attributes, values, etc.). We define the keyword
mapping problem as a sequence tagging problem, and propose a novel deep
learning based supervised approach that utilizes POS tags of NLQs. Our proposed
approach, called \textit{DBTagger} (DataBase Tagger), is an end-to-end and
schema independent solution, which makes it practical for various relational
databases. We evaluate our approach on eight different datasets, and report new
state-of-the-art accuracy results, $92.4\%$ on the average. Our results also
indicate that DBTagger is faster than its counterparts up to $10000$ times and
scalable for bigger databases.
