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NL4DV is a Python toolkit that takes a natural language (NL) query about a given dataset as input and outputs a structured JSON object containing Data attributes, Analytic tasks, and Visualizations (Vega-Lite specifications).

Project description

NL4DV: Natural Language toolkit for Data Visualization

DOI:10.1109/TVCG.2020.3030418 arxiv badge arxiv badge PyPI license PyPI - Downloads

NL4DV takes a natural language query about a given dataset as input and outputs a structured JSON object containing:

  • Data attributes,
  • Analytic tasks, and
  • Visualizations (Vega-Lite specifications)

With this output, developers can

  • Create visualizations in Python using natural language, and/or
  • Add a natural language interface to their existing visualization systems.

NL4DV Overview

Setup Instructions, API Documentation, and Examples

These can all be found on NL4DV's project website.

Credits

NL4DV was created by Arpit Narechania, Arjun Srinivasan, Rishab Mitra, Alex Endert, and John Stasko of the Georgia Tech Visualization Lab.

We thank the members of the Georgia Tech Visualization Lab for their support and constructive feedback.

Citations

2020 IEEE TVCG Journal Full Paper (Proceedings of the 2020 IEEE VIS Conference)

@article{narechania2020nl4dv,
  title = {{NL4DV}: A {Toolkit} for Generating {Analytic Specifications} for {Data Visualization} from {Natural Language} Queries},
  shorttitle = {{NL4DV}},
  author = {{Narechania}, Arpit and {Srinivasan}, Arjun and {Stasko}, John},
  journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)},
  doi = {10.1109/TVCG.2020.3030378},
  year = {2020},
  publisher = {IEEE}
}

2022 IEEE VIS Conference Short Paper Track

@inproceedings{mitra2022conversationalinteraction,
  title = {{Facilitating Conversational Interaction in Natural Language Interfaces for Visualization}},
  author = {{Mitra}, Rishab and {Narechania}, Arpit and {Endert}, Alex and {Stasko}, John},
  booktitle={2022 IEEE Visualization Conference (VIS)},
  url = {https://doi.org/10.48550/arXiv.2207.00189},
  doi = {10.48550/arXiv.2207.00189},
  year = {2022},
  publisher = {IEEE}
}

License

The software is available under the MIT License.

Contact

If you have any questions, feel free to open an issue or contact Arpit Narechania.

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