Named entity recognition (NER) is the task of tagging entities in text with their corresponding type. Approaches typically use BIO notation, which differentiates the beginning (B) and the inside (I) of entities. O is used for non-entity tokens.
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When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive.
#2 best model for Nested Named Entity Recognition on ACE 2004
Therefore, we manually correct these label mistakes and form a cleaner test set.
Recent researches prevalently used BiLSTM-CNN as a core module for NER in a sequence-labeling setup.
#3 best model for Named Entity Recognition on Long-tail emerging entities
Identifying the named entities mentioned in text would enrich many semantic applications at the downstream level.
Most state-of-the-art models for named entity recognition (NER) rely on the availability of large amounts of labeled data, making them challenging to extend to new, lower-resourced languages.
We propose two neural network architectures for nested named entity recognition (NER), a setting in which named entities may overlap and also be labeled with more than one label.
We also investigate the effects of a small amount of additional pretraining on PubMed content, and of combining FLAIR and ELMO models.
SOTA for Named Entity Recognition on BC5CDR (using extra training data)