DeepIE/docs/各主流方法在中文NER上的表现情况.md

• 各主流方法在主要中文NER数据集上的表现情况

	lexicon	Ontonotes	MSRA	Resume	Weibo
biLSTM	----	71.81	91.87	94.41	56.75
Lattice LSTM	词表1	73.88	93.18	94.46	58.79
WC-LSTM	词表1	74.43	93.36	94.96	49.86
LR-CNN	词表1	74.45	93.71	95.11	59.92
CGN	词表2	74.79	93.47	94.12	63.09
LGN	词表1	74.85	93.63	95.41	60.15
Simple-Lexicon	词表1	75.54	93.50	95.59	61.24
FLAT	词表1	76.45	94.12	95.45	60.32
FLAT	词表2	75.70	94.35	94.93	63.42
BERT	----	80.14	94.95	95.53	68.20
BERT+FLAT	词表1	81.82	96.09	95.86	68.55

• 说明：

  • 词表1为Lattice LSTM 采用的词表
  • 词表2为[论文](Analogical Reasoning on Chinese Morphological and Semantic Relations)采用的词表

• 具体方法对应的文献为：

  [1] Lattice LSTM：Chinese NER Using Lattice LSTM

  [2] LR-CNN:CNN-Based Chinese NER with Lexicon Rethinking

  [3] CGN:Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network

  [4] LGN: A Lexicon-Based Graph Neural Network for Chinese NER

  [5] FLAT: Chinese NER Using Flat-Lattice Transformer

  [6] WC-LSTM: An Encoding Strategy Based Word-Character LSTM for Chinese NER Lattice LSTM

  [7] Multi-digraph: A Neural Multi-digraph Model for Chinese NER with Gazetteers

  [8] Simple-Lexicon：Simplify the Usage of Lexicon in Chinese NER