  Recently, many works have tried to augment the performance of Chinese named
entity recognition (NER) using word lexicons. As a representative, Lattice-LSTM
(Zhang and Yang, 2018) has achieved new benchmark results on several public
Chinese NER datasets. However, Lattice-LSTM has a complex model architecture.
This limits its application in many industrial areas where real-time NER
responses are needed.
  In this work, we propose a simple but effective method for incorporating the
word lexicon into the character representations. This method avoids designing a
complicated sequence modeling architecture, and for any neural NER model, it
requires only subtle adjustment of the character representation layer to
introduce the lexicon information. Experimental studies on four benchmark
Chinese NER datasets show that our method achieves an inference speed up to
6.15 times faster than those of state-ofthe-art methods, along with a better
performance. The experimental results also show that the proposed method can be
easily incorporated with pre-trained models like BERT.
