#sql

Text2SQLNet: Syntax Type-Aware Tree Networks for Text-to-SQL

01/12/2019

#sql

Dec 1 , 2019 read

Discover our conference paper Text2SQLNet: Syntax Type-Aware Tree Networks for Text-to-SQL – International Conference Europe Middle East & North Africa Information Systems and Technologies to Support Learning part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 7).

Thanks to the Novelis Research Team for their knowlegde and experience.

Abstract

Building a natural language interface for relational databases is an important and challenging problem in natural language processing (NLP). It requires a system that can understand natural language problems and generate corresponding SQL queries. In this article, we propose the idea of ​​using type information and database content to better understand the rare entities and numbers in natural language problems to improve the model SyntaxSQLNet as the latest technology in Text-to-SQL tasks. We also showed the global architecture and technologies that can be used to implement our neural network (NN) model Text2SQLNet, and integrated our ideas, including using type information to better understand rare entities and numbers in natural language problems. If the format of the user query is incorrect, we can also use the database content to better understand the user query. The realization of this idea can further improve the performance in Text-to-SQL tasks.

About the study

“Relational databases store a vast amount of today’s information and provide the foundation of applications such as medical records (Hillestad et al., 2005)[1], financial markets (Beck and al., 2000)[2], and customer relations management (Ngai et al., 2009)[3]. However, accessing relational databases requires an understanding of query languages such as SQL, which, while powerful, is difficult to master. Natural language interfaces (NLI), a research area at the intersection of natural language processing and human- computer interactions, seeks to provide means for humans to interact with computers through the use of natural language (Androutsopoulos et al., 1995)[4]. Natural language always contains ambiguities, each user can express himself in his own way.”

Read the full article

Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 7)

SpringerLink provid researchers with access to millions of scientific documents from journals, books, series, protocols, reference works and proceedings.

Recent blogs

Top 10 great language models that have transformed NLP in the last 5 years

GPT-4, released by OpenAI in 2023, is the language model that holds one of the largest neural netwo...

Novelis developed a ChatGPT connector for SS&C Blue Prism 

With the rapid advancement of technology, businesses are constantly striving to streamline their pr...

How to effectively manage your Service Level Agreement (SLA) with process intelligence?

Whether you are an operational center of excellence, a customer service department, or a Shared Ser...