[Title] MTAAL: Multi-Task Adversarial Active Learning for Medical Named Entity Recognition and Normalization

[Authors] Baohang Zhou, Xiangrui Cai, Ying Zhang, Wenya Guo, Xiaojie Yuan

AAAI 2021 paper [video]

1. Clone the repo to your local.
2. Download Python version: 3.6.5.
3. Download the word embeddings from the following websites. Put them into the "pretrain" folder. (Word2Vec and Glove)
4. Open the shell or cmd in this repo folder. Run this command to install necessary packages.

pip install -r requirements.txt

1. Before running models, you should run this command to handle the dataset. You can choose the parameters to change datasets and word embeddings.

python preprocess.py --dataset=[ncbi, cdr] --wordembedding=[word2vec, glove]

2. You can input the following command to run the different active learning models. There are different choices for some parameters shown in []. The meaning of these parameters are shown in the following tables.

python main.py params \
--epoch=70 \
--label=0.2 \
--unlabel=0.7 \
--test=0.1 \
--batch_size=32 \
--query_num=64 \
--ad_task=[True, False] \
--dataset=[ncbi, cdr] \
--rnn_units=64 \
--task=[all, ner, nen] \
--gpu=[True, False] \
--al=[diversity, random, lc, entropy, mnlp]

3. After running the model, the test result is saved in the "results" folder.

PS: We use the evaluation metrics as described in this paper (Zhao et al.).