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 split 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

1. Download the character embeddings and word embeddings from Lattice LSTM and put them in the data folder.
2. Download the four datasets in data/MSRANER, data/OntoNotesNER, data/ResumeNER and data/WeiboNER, respectively.
3. To train on the four datasets:

• To train on OntoNotes:

python main.py --train data/OntoNotesNER/train.char.bmes --dev data/OntoNotesNER/dev.char.bmes --test data/OntoNotesNER/test.char.bmes --modelname OntoNotes --savedset data/OntoNotes.dset

• To train on Resume:

python main.py --train data/ResumeNER/train.char.bmes --dev data/ResumeNER/dev.char.bmes --test data/ResumeNER/test.char.bmes --modelname Resume --savedset data/Resume.dset --hidden_dim 200

• To train on Weibo:

python main.py --train data/WeiboNER/train.all.bmes --dev data/WeiboNER/dev.all.bmes --test data/WeiboNER/test.all.bmes --modelname Weibo --savedset data/Weibo.dset --lr=0.005 --hidden_dim 200

• To train on MSRA:

python main.py --train data/MSRANER/train.char.bmes --dev data/MSRANER/dev.char.bmes --test data/MSRANER/test.char.bmes --modelname MSRA --savedset data/MSRA.dset

4. To train/test your own data: modify the command with your file path and run.