Semantic Parsing to Manipulate Relational Database For a Management System
This work addresses the need for efficient and data-light semantic parsing in management systems, though it appears incremental by building on existing datasets and methods.
The paper tackles the problem of converting human language to SQL queries for relational databases, proposing a simple algorithm that reduces computational complexity and data requirements while achieving results through basic NLP tasks.
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.