havelhakimi/HTLA-n
Code for the paper "Local Hierarchy-Aware Text-Label Association for Hierarchical Text Classification" acepted in DSAA 2024
This project helps data scientists, researchers, and content managers accurately categorize documents into multi-level, nested topics. You input a collection of text documents and a defined hierarchy of labels, and it outputs predictions for which category (or categories) each document belongs to, organized by their hierarchical structure. This is designed for anyone needing to classify texts into complex, tree-like taxonomies.
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Use this if you need to classify documents into a hierarchical structure and want to leverage the relationships between those categories for better accuracy.
Not ideal if your classification task involves flat, non-hierarchical categories or if you don't have existing labeled data for training.
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Python
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Mar 18, 2025
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