https://github.com/aida-ugent/cross-domain-htc

https://github.com/aida-ugent/cross-domain-htc

Science Score: 36.0%

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    Links to: arxiv.org
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Last synced: 11 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: aida-ugent
  • License: agpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 33.2 KB
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Created over 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme License

README.md

Cross-Domain Resources for Text Classification with Hierarchical Labels

This repository is the companion for the paper ``Your Next State-of-the-Art Could Come from Another Domain: A Cross-Domain Analysis of Hierarchical Text Classification.''

arXiv

📊 Datasets

We provide a collection of seven diverse datasets for hierarchical text classification, spanning legal, scientific, medical, and patent domains. Each dataset comes with gold-standard taxonomies, making them ideal for developing and evaluating hierarchical text classification methods.

| Dataset | Domain | Documents | Labels | Hierarchy Depth | Avg Length | |---------|--------|-----------|---------|----------------|------------| | EurLex-3985 | Legal | 19,306 | 3,985 | 2 | 2,635 | | EurLex-DC-410 | Legal | 19,340 | 410 | 2 | 2,635 | | WOS-141 | Scientific | 46,985 | 141 | 2 | 200 | | SciHTC-83 | Scientific | 186,160 | 83 | 6 | 145 | | SciHTC-800 | Scientific | 186,160 | 800 | 6 | 145 | | MIMIC3-3681 | Medical | 52,712 | 3,681 | 3* | 1,514 | | USPTO2M-632 | Patent | 1,998,408 | 632 | 2* | 117 |

* Expanded hierarchy for certain methods (see paper for details)

🚀 Getting Started

Please fill the Consent Form to get access to the datasets.

💻 Code

Please see src/README.md for more details.

📚 Citation

If you find this repository useful, please cite our paper:

@misc{li2024stateoftheartcomedomaincrossdomain, title={Your Next State-of-the-Art Could Come from Another Domain: A Cross-Domain Analysis of Hierarchical Text Classification}, author={Nan Li and Bo Kang and Tijl De Bie}, year={2024}, eprint={2412.12744}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.12744}, }

Owner

  • Name: Ghent University Artificial Intelligence & Data Analytics Group
  • Login: aida-ugent
  • Kind: organization
  • Email: tijl.debie@ugent.be
  • Location: Ghent

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Nan Li n****i@u****e 7
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