基于BERT和知识图谱的中文电子病例医学命名实体识别项目源码.zip

# K-BERT  Sorce code and datasets for ["K-BERT: Enabling Language Representation with Knowledge Graph"](https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuW.5594.pdf), which is implemented based on the [UER](https://github.com/dbiir/UER-py) framework. ## Requirements Software: ``` Python3 Pytorch >= 1.0 argparse == 1.1 ``` ## Prepare * Download the ``google_model.bin`` from [here](https://share.weiyun.com/5GuzfVX), and save it to the ``models/`` directory. * Download the ``CnDbpedia.spo`` from [here](https://share.weiyun.com/5BvtHyO), and save it to the ``brain/kgs/`` directory. * Optional - Download the datasets for evaluation from [here](https://share.weiyun.com/5Id9PVZ), unzip and place them in the ``datasets/`` directory. The directory tree of K-BERT: ``` K-BERT ├── brain │   ├── config.py │   ├── __init__.py │   ├── kgs │   │   ├── CnDbpedia.spo │   │   ├── HowNet.spo │   │   └── Medical.spo │   └── knowgraph.py ├── datasets │   ├── book_review │   │   ├── dev.tsv │   │   ├── test.tsv │   │   └── train.tsv │   ├── chnsenticorp │   │   ├── dev.tsv │   │   ├── test.tsv │   │   └── train.tsv │ ... │ ├── models │   ├── google_config.json │   ├── google_model.bin │   └── google_vocab.txt ├── outputs ├── uer ├── README.md ├── requirements.txt ├── run_kbert_cls.py └── run_kbert_ner.py ``` ## K-BERT for text classification ### Classification example Run example on Book review with CnDbpedia: ```sh CUDA_VISIBLE_DEVICES='0' nohup python3 -u run_kbert_cls.py \ --pretrained_model_path ./models/google_model.bin \ --config_path ./models/google_config.json \ --vocab_path ./models/google_vocab.txt \ --train_path ./datasets/book_review/train.tsv \ --dev_path ./datasets/book_review/dev.tsv \ --test_path ./datasets/book_review/test.tsv \ --epochs_num 5 --batch_size 32 --kg_name CnDbpedia \ --output_model_path ./outputs/kbert_bookreview_CnDbpedia.bin \ > ./outputs/kbert_bookreview_CnDbpedia.log & ``` Results: ``` Best accuracy in dev : 88.80% Best accuracy in test: 87.69% ``` Options of ``run_kbert_cls.py``: ``` useage: [--pretrained_model_path] - Path to the pre-trained model parameters. [--config_path] - Path to the model configuration file. [--vocab_path] - Path to the vocabulary file. --train_path - Path to the training dataset. --dev_path - Path to the validating dataset. --test_path - Path to the testing dataset. [--epochs_num] - The number of training epoches. [--batch_size] - Batch size of the training process. [--kg_name] - The name of knowledge graph, "HowNet", "CnDbpedia" or "Medical". [--output_model_path] - Path to the output model. ``` ### Classification benchmarks Accuracy (dev/test %) on different dataset: | Dataset | HowNet | CnDbpedia | | :----- | :----: | :----: | | Book review | 88.75/87.75 | 88.80/87.69 | | ChnSentiCorp | 95.00/95.50 | 94.42/95.25 | | Shopping | 97.01/96.92 | 96.94/96.73 | | Weibo | 98.22/98.33 | 98.29/98.33 | | LCQMC | 88.97/87.14 | 88.91/87.20 | | XNLI | 77.11/77.07 | 76.99/77.43 | ## K-BERT for named entity recognization (NER) ### NER example Run an example on the msra_ner dataset with CnDbpedia: ``` CUDA_VISIBLE_DEVICES='0' nohup python3 -u run_kbert_ner.py \ --pretrained_model_path ./models/google_model.bin \ --config_path ./models/google_config.json \ --vocab_path ./models/google_vocab.txt \ --train_path ./datasets/msra_ner/train.tsv \ --dev_path ./datasets/msra_ner/dev.tsv \ --test_path ./datasets/msra_ner/test.tsv \ --epochs_num 5 --batch_size 16 --kg_name CnDbpedia \ --output_model_path ./outputs/kbert_msraner_CnDbpedia.bin \ > ./outputs/kbert_msraner_CnDbpedia.log & ``` Results: ``` The best in dev : precision=0.957, recall=0.962, f1=0.960 The best in test: precision=0.953, recall=0.959, f1=0.956 ``` Options of ``run_kbert_ner.py``: ``` useage: [--pretrained_model_path] - Path to the pre-trained model parameters. [--config_path] - Path to the model configuration file. [--vocab_path] - Path to the vocabulary file. --train_path - Path to the training dataset. --dev_path - Path to the validating dataset. --test_path - Path to the testing dataset. [--epochs_num] - The number of training epoches. [--batch_size] - Batch size of the training process. [--kg_name] - The name of knowledge graph. [--output_model_path] - Path to the output model. ``` ## K-BERT for domain-specific tasks Experimental results on domain-specific tasks (Precision/Recall/F1 %): | KG | Finance_QA | Law_QA | Finance_NER | Medicine_NER | | :----- | :----: | :----: | :----: | :----: | | HowNet | 0.805/0.888/0.845 | 0.842/0.903/0.871 | 0.860/0.888/0.874 | 0.935/0.939/0.937 | | CN-DBpedia | 0.814/0.881/0.846 | 0.814/0.942/0.874 | 0.860/0.887/0.873 | 0.935/0.937/0.936 | | MedicalKG | -- | -- | -- | 0.944/0.943/0.944 | ## Acknowledgement This work is a joint study with the support of Peking University and Tencent Inc. If you use this code, please cite this paper: ``` @inproceedings{weijie2019kbert, title={{K-BERT}: Enabling Language Representation with Knowledge Graph}, author={Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, Ping Wang}, booktitle={Proceedings of AAAI 2020}, year={2020} } ```