Review of Research on Natural Language Interfaces for Data Visualization

被引：0

作者：

Gao, Shuai ^{[1
,2
]}

Xi, Xuefeng ^{[1
,2
,3
]}

Zheng, Qian ^{[1
,2
]}

Cui, Zhiming ^{[1
,2
,3
]}

Sheng, Shengli ^{[4
]}

机构：

[1] School of Electronic & Information Engineering, Suzhou University of Science and Technology, Jiangsu, Suzhou,215000, China

[2] Suzhou Key Laboratory of Virtual Reality Intelligent Interaction and Application Technology, Jiangsu, Suzhou,215000, China

[3] Suzhou Smart City Research Institute, Jiangsu, Suzhou,215000, China

[4] Texas Institute of Technology, Lubbock,TX,79401, United States

来源：

Computer Engineering and Applications | 2024年 / 60卷 / 15期

关键词：

Neural network models;

D O I：

10.3778/j.issn.1002-8331.2310-0167

中图分类号：

学科分类号：

摘要：

The long-standing goal in the field of data visualization has been to find a solution that directly generates visualizations from natural language. Research on natural language interfaces (NLI) provides a new approach to this field. This interface accepts queries in the form of natural language and tabular datasets as input and outputs corresponding visualization renderings. Simultaneously, as an auxiliary input method, traditional users need to convert analytical intents into a series of logical operations and interact with them, such as programming instructions or graphical interface operations. Combining the use of natural language interfaces for data visualization (DV-NLI) enables users to focus on visualization tasks without worrying about how to operate visualization tools. In recent years, with the rise of large language models (LLM) such as GPT-3 and GPT-4, research on integrating LLM with visualization has become a hot topic. This paper provides a comprehensive review of existing DV-NLIs and supplements them with the latest research. Based on their implementation methods, DV-NLIs are categorized into symbolic NLP methods, deep learning model methods, and large language model methods. It also analyzes and discusses relevant techniques under each category. Finally, the paper summarizes and looks forward to future work in DV-NLI. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：24 / 41