yuzhimanhua/MICoL
Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification (WWW'22)
This project helps researchers and academics categorize scientific papers quickly and accurately, even for new topics they haven't seen before. It takes a paper's text and its associated metadata (like authors, venue, references) and outputs a list of relevant subject labels. This is ideal for scientists, librarians, or information managers who need to organize large collections of academic literature.
No commits in the last 6 months.
Use this if you need to automatically assign subject categories to a large volume of scientific papers, especially when dealing with topics that may not have many previously labeled examples.
Not ideal if your classification task does not involve rich metadata (authors, citations, venues) or if you are working with non-academic text.
Stars
32
Forks
5
Language
Python
License
Apache-2.0
Category
Last pushed
Jun 21, 2025
Commits (30d)
0
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