  Chatbots and AI assistants have claimed their importance in today life. The
main reason behind adopting this technology is to connect with the user,
understand their requirements, and fulfill them. This has been achieved but at
the cost of heavy training data and complex learning models. This work is
carried out proposes a simple algorithm, a model which can be implemented in
different fields each with its own work scope. The proposed model converts
human language text to computer-understandable SQL queries. The model requires
data only related to the specific field, saving data space. This model performs
linear computation hence solving the computational complexity. This work also
defines the stages where a new methodology is implemented and what previous
method was adopted to fulfill the requirement at that stage. Two datasets
available online will be used in this work, the ATIS dataset, and WikiSQL. This
work compares the computation time among the 2 datasets and also compares the
accuracy of both. This paper works over basic Natural language processing tasks
like semantic parsing, NER, parts of speech and tends to achieve results
through these simple methods.
