• Corpus ID: 221739266

Cascaded Models for Better Fine-Grained Named Entity Recognition

@article{Awasthy2020CascadedMF,
  title={Cascaded Models for Better Fine-Grained Named Entity Recognition},
  author={Parul Awasthy and Taesun Moon and Jian Ni and Radu Florian},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.07317}
}
Named Entity Recognition (NER) is an essential precursor task for many natural language applications, such as relation extraction or event extraction. Much of the NER research has been done on datasets with few classes of entity types (e.g. PER, LOC, ORG, MISC), but many real world applications (disaster relief, complex event extraction, law enforcement) can benefit from a larger NER typeset. More recently, datasets were created that have hundreds to thousands of types of entities, sparking new… 
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Fine-grained Named Entity Recognition using only Coarse-grained labels

TLDR
Fine-grained NER is necessary to get a better understanding of the entities and should further help with the information extraction, given only the entities’ coarse- grained labels.

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