zliucr/CrossNER

CrossNER: Evaluating Cross-Domain Named Entity Recognition (AAAI-2021)

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This project provides a comprehensive collection of named entity recognition (NER) datasets across five distinct fields: Politics, Natural Science, Music, Literature, and Artificial Intelligence. It takes raw text from these domains and outputs identified entities with specialized categories for each field. Data scientists and NLP researchers focused on domain adaptation for NER would find this valuable.

134 stars. No commits in the last 6 months.

Use this if you need high-quality, labeled text data to train or evaluate named entity recognition models across diverse domains, especially when dealing with domain-specific terminology.

Not ideal if you are looking for a pre-built, ready-to-use NER model rather than data for training and evaluation.

named-entity-recognition NLP-data domain-adaptation text-analysis information-extraction

Stale 6m No Package No Dependents

Maintenance 0 / 25

Adoption 10 / 25

Maturity 16 / 25

Community 19 / 25

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Stars

134

Forks

Language

Python

License

MIT

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