MultiCoNER Dataset

Description

MultiCoNER 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.

Update Frequency

N/A

License

CC BY 4.0

Documentation

https://multiconer.s3.us-west-2.amazonaws.com/readme.html

Managed By

See all datasets managed by Amazon.

Contact

njfn@amazon.com

How to Cite

MultiCoNER Dataset was accessed on DATE from https://registry.opendata.aws/multiconer.

Usage Examples

Publications

• Dynamic Gazetteer Integration in Multilingual Models for Cross-Lingual and Cross-Domain Named Entity Recognition by Besnik Fetahu, Anjie Fang, Oleg Rokhlenko and Shervin Malmasi
  
• Gazetteer Enhanced Named Entity Recognition for Code-Mixed Web Queries by Besnik Fetahu, Anjie Fang, Oleg Rokhlenko and Shervin Malmasi
  
• GEMNET: Effective Gated Gazetteer Representations for Recognizing Complex Entities in Low-context Input by Tao Meng, Anjie Fang, Oleg Rokhlenko and Shervin Malmasi