FeiWang96/GTR
[SIGIR 2021] Retrieving Complex Tables with Multi-Granular Graph Representation Learning.
This project helps researchers and data scientists working with complex tabular data to efficiently find relevant tables based on a query. You input a textual query and a collection of tables, and it outputs a ranked list of tables most pertinent to your query. It's designed for those who need to retrieve specific information from large datasets of structured tables, such as those found in scientific publications or public data portals.
No commits in the last 6 months.
Use this if you need to precisely retrieve complex tables from a large corpus using natural language queries, especially in research or data analysis settings.
Not ideal if you are looking for a simple keyword search across unstructured text or need to join tables rather than retrieve them.
Stars
48
Forks
5
Language
Python
License
Apache-2.0
Category
Last pushed
Sep 14, 2022
Commits (30d)
0
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