This is the implementation of our arxiv paper "Simplify the Usage of Lexicon in Chinese NER", which rejects complicated operations for incorporating word lexicon in Chinese NER. We show that incorporating lexicon in Chinese NER can be quite simple and, at the same time, effective.

====== Python 3.6 Pytorch 0.4.1

====== CoNLL format, with each character and its label splited by a whitespace in a line. The "BMES" tag scheme is prefered.

别 O 错 O 过 O 邻 O 近 O 大 B-LOC 鹏 M-LOC 湾 E-LOC 的 O 湿 O 地 O

====== The pretrained embeddings(word embedding, char embedding and bichar embedding) are the same with Lattice LSTM(https://github.com/jiesutd/LatticeLSTM)

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1. Download the character embeddings and word embeddings and put them in the data folder.
2. To train/test the demo on OntoNotes: sh train.sh / sh test.sh
3. To train/test the demo on the other three datasets: change the learning rate and lstm hidden dimension according to the paper and run sh train.sh / sh test.sh
4. To train/test your own data: modify the 'train.sh' or 'test.sh' file with your file path, and run the shell file.