MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition
This dataset addresses the problem of robust NER for researchers and practitioners dealing with low-context, multilingual, and syntactically complex text, though it is incremental as it builds on existing data compilation methods.
The authors tackled the challenge of Named Entity Recognition (NER) in complex, multilingual contexts by creating MultiCoNER, a large-scale dataset covering 11 languages and 3 domains, which showed that a state-of-the-art model with gazetteers improved macro-F1 by 30% over a baseline.
We present MultiCoNER, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets. This dataset is designed to represent contemporary challenges in NER, including low-context scenarios (short and uncased text), syntactically complex entities like movie titles, and long-tail entity distributions. The 26M token dataset is compiled from public resources using techniques such as heuristic-based sentence sampling, template extraction and slotting, and machine translation. We applied two NER models on our dataset: a baseline XLM-RoBERTa model, and a state-of-the-art GEMNET model that leverages gazetteers. The baseline achieves moderate performance (macro-F1=54%), highlighting the difficulty of our data. GEMNET, which uses gazetteers, improvement significantly (average improvement of macro-F1=+30%). MultiCoNER poses challenges even for large pre-trained language models, and we believe that it can help further research in building robust NER systems. MultiCoNER is publicly available at https://registry.opendata.aws/multiconer/ and we hope that this resource will help advance research in various aspects of NER.