yuzhimanhua/MATCH
MATCH: Metadata-Aware Text Classification in A Large Hierarchy (WWW'21)
This project helps researchers, librarians, and information scientists automatically categorize academic papers and medical articles into detailed, hierarchical subject areas. It takes the full text of articles, along with metadata like authors, venues, and references, and outputs a list of relevant subject labels organized in a taxonomy. It's designed for anyone needing to accurately classify large volumes of text in scientific or medical domains.
117 stars. No commits in the last 6 months.
Use this if you need to automatically assign fine-grained, structured categories to scientific papers or medical articles, leveraging not just the content but also contextual information about the document.
Not ideal if your classification task involves a flat list of categories, doesn't benefit from metadata like authors or references, or if you're working outside academic or medical research domains.
Stars
117
Forks
18
Language
Python
License
Apache-2.0
Category
Last pushed
Apr 02, 2024
Commits (30d)
0
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