• NER: In medical field or other field, values of unannotated samples were ignored. This algorithm use adversarial idea to use unannotated to maximize NER performances， which means both unannotated and annotated samples are untilized for NER.
• Active leanring: Use both ouputs of CRF layer and the discriminator to select unannotated samples to be labeled.
• Identification of error labels: The discriminator outputs smaller score for the sequences with error labels.

• Python: 3.6
• Tensorflow: 1.15.2

The DataSet should be in the format of two columns. The words are in the first column and labels are in the second column. Sample data is in the Data Directory.
The china_medical_char_data_cleaned directory processes Chinese data and medical_char_data_cleaned processes English data. First, Download dataset and put dataset into these two directories based on languages. Then put your embedding files into these dicrectories and run .sh file to produce training data, validation data, test data and embedding npz.

bash makefile.sh

Download pretrained Bert and divide you training data into two parts: labeled data and unlabeled data. Put your divided training data、validation data and test data into model/data_path. (pretrained Bert: https://github.com/google-research/bert)
Train data

python main.py --mode=train

One of effective optimize methods is to update different layers of BERT, you can set up which layers are updated in the bert_gan file.For example,

self.params_1 = [param for param in tf.trainable_variables() if
                         ('crf_param' not in param.name and 'bert/encoder/layer_11/output' in param.name) ]
grads_and_vars2 = optim.compute_gradients(self.loss2, self.params_1)

Test data and carry out active learning steps

python main.py --mode=test

[BERT] (https://github.com/google-research/bert)
[BiLSTM-CRF] (https://github.com/guillaumegenthial/sequence_tagging) (https://github.com/guillaumegenthial/tf_ner/tree/master/models/chars_lstm_lstm_crf)

.