MultiCoNER 1 is a large multilingual dataset (11 languages) for Named Entity Recognition. It is designed to represent some of the contemporary challenges in NER, including low-context scenarios (short and uncased text), syntactically complex entities such as movie titles, and long-tail entity distributions. MultiCoNER 2 is a large multilingual dataset (12 languages) for fine grained Named Entity Recognition. Its fine-grained taxonomy contains 36 NE classes, representing real-world challenges for NER, where named entities, apart from the surface form, context represents a critical role in distinguishing between the different fine-grained types (e.g. Scientist vs. Athlete). Furthermore, the test data of MultiCoNER 2 contains noisy instances, where the noise has been applied to both context tokens as well as the entity tokens. The noise includes typing errors at character level based on keyboard layouts in the the different languages.
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MultiCoNER Datasets was accessed on
DATE from https://registry.opendata.aws/multiconer.
aws s3 ls --no-sign-request s3://multiconer/multiconer2022/
aws s3 ls --no-sign-request s3://multiconer/multiconer2023/