utahnlp/knowledge_infotabs

Repository containing code for the NAACL 2021 paper (Incorporating External Knowledge to Enhance Tabular Reasoning)

37
/ 100
Emerging

This project helps data analysts and researchers automatically interpret and reason with information presented in tables. It takes raw tabular data and associated text descriptions, processes them to identify key relationships and facts, and then generates structured outputs that explain the connections. This is especially useful for anyone who needs to quickly understand complex datasets and draw conclusions without extensive manual review.

No commits in the last 6 months.

Use this if you need to extract logical conclusions and insights from semi-structured tables by incorporating external knowledge for better reasoning.

Not ideal if your data is unstructured free-form text or if you only need basic data extraction without complex inferential reasoning.

data-analysis tabular-data knowledge-extraction automated-reasoning data-interpretation
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 15 / 25

How are scores calculated?

Stars

17

Forks

5

Language

Python

License

MIT

Last pushed

Jun 20, 2021

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/nlp/utahnlp/knowledge_infotabs"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.