IntLawNER: A Named Entity Recognition Dataset and Benchmark in International Law
Closing the digital divide in international legal analysis with a new standard for entity extraction.
International law provides the normative framework through which states coordinate action, regulate armed conflict, and protect human rights, yet its texts remain without token-level named entity recognition (NER) resources. We introduce IntLawNER, a NER dataset and benchmark for...
ArXiv CS.AI
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IntLawNER bridges a critical gap by providing the structured linguistic data necessary for specialized legal AI to navigate complex international treaties and precedents. By enabling automated entity extraction, this benchmark empowers researchers to map global legal frameworks with unprecedented speed and accuracy.
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