Named entity recognition

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.

Example:

Mark	Watney	visited	Mars
B-PER	I-PER	O	B-LOC

CoNLL 2003

The CoNLL 2003 NER task consists of newswire text from the Reuters RCV1 corpus tagged with four different entity types (PER, LOC, ORG, MISC). Models are evaluated based on span-based F1.

Model	F1	Paper / Source
BiLSTM-CRF+ELMo (Peters et al., 2018)	92.22	Deep contextualized word representations
Peters et al. (2017)	91.93	Semi-supervised sequence tagging with bidirectional language models
Yang et al. (2017)	91.26	Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
Ma and Hovy (2016)	91.21	End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
LSTM-CRF (Lample et al., 2016)	90.94	Neural Architectures for Named Entity Recognition

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