BiLSTM-CNN-CRF

Reported on 1 benchmark across 1 task · 1 paper · 1 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing1 result

Named Entity Recognition (NER)onCoNLL++
F1· 2016-03-04
91.87
best: 96.12 (LUKE + SubRegWeigh (K-means))
SOTA
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF arXiv:1603.01354