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Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data

arXiv

This is the code base for weakly supervised NER.

We provide a three stage framework:

  • Stage I: Domain continual pre-training;
  • Stage II: Noise-aware weakly supervised pre-training;
  • Stage III: Fine-tuning.

In this code base, we actually provide basic building blocks which allow arbitrary combination of different stages. We also provide examples scripts for reproducing our results in BioMedical NER.

See details in arXiv.

Performance Benchmark

BioMedical NER

Method (F1) BC5CDR-chem BC5CDR-disease NCBI-disease
BERT 89.99 79.92 85.87
bioBERT 92.85 84.70 89.13
PubMedBERT 93.33 85.62 87.82
Ours 94.17 90.69 92.28

See more in bio_script/README.md

Dependency

pytorch==1.6.0
transformers==3.3.1
allennlp==1.1.0
flashtool==0.0.10
ray==0.8.7

Install requirements

pip install -r requirements.txt

(If the allennlp and transformers are incompatible, install allennlp first and then update transformers. Since we only use some small functions of allennlp, it should works fine. )

File Structure:

├── bert-ner          #  Python Code for Training NER models
│   └── ...
└── bio_script        #  Shell Scripts for Training BioMedical NER models
    └── ...

Usage

See examples in bio_script

Hyperparameter Explaination

Here we explain hyperparameters used the scripts in ./bio_script.

Training Scripts:

Scripts

  • roberta_mlm_pretrain.sh
  • weak_weighted_selftrain.sh
  • finetune.sh

Hyperparameter

  • GPUID: Choose the GPU for training. It can also be specified by xxx.sh 0,1,2,3.
  • MASTER_PORT: automatically constructed (avoid conflicts) for distributed training.
  • DISTRIBUTE_GPU: use distributed training or not
  • PROJECT_ROOT: automatically detected, the root path of the project folder.
  • DATA_DIR: Directory of the training data, where it contains train.txt test.txt dev.txt labels.txt weak_train.txt (weak data) aug_train.txt (optional).
  • USE_DA: if augment training data by augmentation, i.e., combine train.txt + aug_train.txt in DATA_DIR for training.
  • BERT_MODEL: the model backbone, e.g., roberta-large. See transformers for details.
  • BERT_CKP: see BERT_MODEL_PATH.
  • BERT_MODEL_PATH: the path of the model checkpoint that you want to load as the initialization. Usually used with BERT_CKP.
  • LOSSFUNC: nll the normal loss function, corrected_nll noise-aware risk (i.e., add weighted log-unlikelihood regularization: wei*nll + (1-wei)*null ).
  • MAX_WEIGHT: The maximum weight of a sample in the loss.
  • MAX_LENGTH: max sentence length.
  • BATCH_SIZE: batch size per GPU.
  • NUM_EPOCHS: number of training epoches.
  • LR: learning rate.
  • WARMUP: learning rate warmup steps.
  • SAVE_STEPS: the frequency of saving models.
  • EVAL_STEPS: the frequency of testing on validation.
  • SEED: radnom seed.
  • OUTPUT_DIR: the directory for saving model and code. Some parameters will be automatically appended to the path.
    • roberta_mlm_pretrain.sh: It's better to manually check where you want to save the model.]
    • finetune.sh: It will be save in ${BERT_MODEL_PATH}/finetune_xxxx.
    • weak_weighted_selftrain.sh: It will be save in ${BERT_MODEL_PATH}/selftrain/${FBA_RULE}_xxxx (see FBA_RULE below)

There are some addition parameters need to be set for weakly supervised learning (weak_weighted_selftrain.sh).

Profiling Script

Scripts

  • profile.sh

Profiling scripts also use the same entry as the training script: bert-ner/run_ner.py but only do evaluation.

Hyperparameter Basically the same as training script.

  • PROFILE_FILE: can be train,dev,test or a specific path to a txt data. E.g., using Weak by

    PROFILE_FILE=weak_train_100.txt PROFILE_FILE=$DATA_DIR/$PROFILE_FILE

  • OUTPUT_DIR: It will be saved in OUTPUT_DIR=${BERT_MODEL_PATH}/predict/profile

Weakly Supervised Data Refinement Script

Scripts

  • profile2refinedweakdata.sh

Hyperparameter

  • BERT_CKP: see BERT_MODEL_PATH.
  • BERT_MODEL_PATH: the path of the model checkpoint that you want to load as the initialization. Usually used with BERT_CKP.
  • WEI_RULE: rule for generating weight for each weak sample.
    • uni: all are 1
    • avgaccu: confidence estimate for new labels generated by all_overwrite
    • avgaccu_weak_non_O_promote: confidence estimate for new labels generated by non_O_overwrite
  • PRED_RULE: rule for generating new weak labels.
    • non_O_overwrite: non-entity ('O') is overwrited by prediction
    • all_overwrite: all use prediction, i.e., self-training
    • no: use original weak labels
    • non_O_overwrite_all_overwrite_over_accu_xx: non_O_overwrite + if confidence is higher than xx all tokens use prediction as new labels

The generated data will be saved in ${BERT_MODEL_PATH}/predict/weak_${PRED_RULE}-WEI_${WEI_RULE} WEAK_RULE specified in weak_weighted_selftrain.sh is essential the name of folder weak_${PRED_RULE}-WEI_${WEI_RULE}.

More Rounds of Training, Try Different Combination

  1. To do training with weakly supervised data from any model checkpoint directory:
  • i) Set BERT_CKP appropriately;
  • ii) Create profile data, e.g., run ./bio_script/profile.sh for dev set and weak set
  • iii) Generate data with weak labels from profile data, e.g., run ./bio_script/profile2refinedweakdata.sh. You can use different rules to generate weights for each sample (WEI_RULE) and different rules to refine weak labels (PRED_RULE). See more details in ./ber-ner/profile2refinedweakdata.py
  • iv) Do training with ./bio_script/weak_weighted_selftrain.sh.
  1. To do fine-tuning with human labeled data from any model checkpoint directory:
  • i) Set BERT_CKP appropriately;
  • ii) Run ./bio_script/finetune.sh.

Reference

@inproceedings{Jiang2021NamedER,
  title={Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data},
  author={Haoming Jiang and Danqing Zhang and Tianyue Cao and Bing Yin and T. Zhao},
  booktitle={ACL/IJCNLP},
  year={2021}
}

Security

See CONTRIBUTING for more information.

License

This library is licensed under the MIT-0 License. See the LICENSE file.