MultiCoNER Datasets

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 disti...