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MultiNERD: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)
2022
Findings of the Association for Computational Linguistics: NAACL 2022
unpublished
Named Entity Recognition (NER) is the task of identifying named entities in texts and classifying them through specific semantic categories, a process which is crucial for a wide range of NLP applications. Current datasets for NER focus mainly on coarse-grained entity types, tend to consider a single textual genre and to cover a narrow set of languages, thus limiting the general applicability of NER systems. In this work, we design a new methodology for automatically producing NER annotations,
doi:10.18653/v1/2022.findings-naacl.60
fatcat:vfye7k6vbza5lpb6kzps5tdi4e