• Leveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically
• • Description
  • Requirements
  • Code Structure:
  • SampleData Description
  • InitModel Description
  • Train Model :
  • Predict on relevance prediction test set & F1 measurement:
  • Predict on matching test set & P@K, NDCG@K, MRR, MAP measurements:
  • Reference

Leveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically

Description

This repository includes the source code for the paper "Leveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically".
Basically, the program takes the medical literature, the RCor text fragment, the KImp text fragment, and the knowledge as input, and predict a label to indicate the relevance degree between the medical literature and the knowledge.
More details about the underneath model can be found in the submitted paper.

Requirements

Libraries: ubuntu = 16.04, cuda = 10.2, cudnn = 8, GPU card = NVIDIA Tesla V100 * 1

Dependencies: python > 3.5, tensorflow > 1.10.0, pdb, numpy, tdqm, codecs

Code Structure:

HiCapsRKL

├── SampleData

│   ├── train.tsv

│   ├── relevance_prediction_test_data

│   │  ├── test.tsv

│   ├── medical_literature_retrieval_test_data

│   │  ├── test.tsv

├── InitModel

│   ├── modellink.txt

├── init.py

├── match_utils.py

├── modeling.py

├── optimization.py

├── tokenization.py

├── train_HiCapsRKL.py

├── f1.py

├── ranking_metrics.py

└── README.md

• "train_HiCapsRKL.py" is the main file for training or test.
• "modeling.py" is the file of the model structure.
• "match_utils.py" is the file containing the capsule network function
• "optimization.py" is the file for optimiztion.
• "tokenization.py" is the file to tokenize the sentences.
• "f1.py" is the file to calculate the macro-f1 and micro-f1 score on the prediction of matching test set.
• "ranking_metrics.py" is the file to calculate the P@K, NDCG@K, MRR, MAP scores on the prediction of ranking test set.

SampleData Description

The training data (train.tsv), the relevance prediction test data (relevance_prediction_test_data/test.tsv), and the medical literature retrieval test data (medical_literature_retrieval_test_data/test.tsv) are randomly sampled from each whole set
and these data could be used to run the training and testing process for this code.

• data format: <label + "\t" + MedL + "\t" + K_str + "\t" + K_idx + "\t" + RCor_score + "\t" + RCor_text_fragment + "\t" + KImp_score + "\t" + KImp_text_fragment + "\t" + keywords + "\t" + MedL_id + "\t" + bk_location>

InitModel Description

The directory contains the BERT-Base, Chinese pre-trained model as the initial checkpoint for training HiCapsRKL. If needed, one can download these paramters from https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip .

Train Model :

python train_HiCapsRKL.py --task_name=medrkg --do_train=true --data_dir=SampleData \
--vocab_file=InitModel/vocab.txt --bert_config_file=InitModel/bert_config.json \
--init_checkpoint=InitModel/bert_model.ckpt --max_seq_length=256 --train_batch_size=8 \
--learning_rate=2e-5 --num_train_epochs=10.0 --output_dir=output_dir/

Predict on relevance prediction test set & F1 measurement:

* python train_HiCapsRKL.py --task_name=medrkg --do_predict=true \
--data_dir=SampleData/relevance_prediction_test_data --vocab_file=InitModel/vocab.txt \
--bert_config_file=InitModel/bert_config.json --init_checkpoint=output_dir/\*\*\*.ckpt \
--output_dir=output_dir/
* python f1.py output_dir

Predict on matching test set & P@K, NDCG@K, MRR, MAP measurements:

* python train_HiCapsRKL.py --task_name=medrkg --do_predict=true \
--data_dir=SampleData/medical_literature_retrieval_test_data \
--vocab_file=InitModel/vocab.txt --bert_config_file=InitModel/bert_config.json \
--init_checkpoint=output_dir/\*\*\*.ckpt --output_dir=output_dir/
* python ranking_metrics.py output_dir

Reference

If you use any of the resources listed here, please cite:

@inproceedings{gdls-2021-HiCapsRKL,
　　title = "Leveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically",
　　author = "Liu, Xin  and Chen, Qingcai  and Chen, Junying and Zhou, Wenxiu  and Liu, Tingyu  and Yang, Xinlan  and Peng, Weihua",
　　booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
　　month = nov,
　　year = "2021",
　　publisher = "Association for Computational Linguistics",
}