https://github.com/islouishsu/span-scheme-for-disc-ner
Official repository for the paper "A Simple but Effective Span-level Tagging Scheme for Discontinuous Named Entity Recognition" (https://link.springer.com/article/10.1007/s00521-024-09454-y).
https://github.com/islouishsu/span-scheme-for-disc-ner
Last synced: 22 days ago
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Official repository for the paper "A Simple but Effective Span-level Tagging Scheme for Discontinuous Named Entity Recognition" (https://link.springer.com/article/10.1007/s00521-024-09454-y).
- Host: GitHub
- URL: https://github.com/islouishsu/span-scheme-for-disc-ner
- Owner: isLouisHsu
- Created: 2023-08-25T13:55:27.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-24T05:50:47.000Z (about 1 year ago)
- Last Synced: 2024-12-26T04:29:14.233Z (5 months ago)
- Language: Python
- Homepage:
- Size: 4.38 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# A Simple but Effective Span-level Tagging Scheme for Discontinuous Named Entity Recognition
Official repository for the paper "A Simple but Effective Span-level Tagging Scheme for Discontinuous Named Entity Recognition" (https://link.springer.com/article/10.1007/s00521-024-09454-y).
## Prepare Data
**Download datasets**
- CADEC dataset can be downloaded at: https://data.csiro.au/dap/landingpage?pid=csiro:10948&v=3&d=true
- ShARe data can be downloaded at: https://physionet.org/
**Download Pretrained BERT Model**
- ClinicalBERT: https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT
- BioBERT: https://huggingface.co/dmis-lab/biobert-base-cased-v1.2
**Preprocessing**
CADEC:
```bash
bash scripts/build_cadec_data_for_discontinuous_ner.sh
```
NOTE: The processing pipeline follows https://github.com/daixiangau/acl2020-transition-discontinuous-ner.
## Run Experiments
```bash
python run_disc_ner.py \
--experiment_code=exp_cadec_biobert_gcn_seed42 \
--task_name=cadec \
--model_type=bert \
--do_lower_case \
--pretrained_model_path=./pretrained/biobert-base-cased-v1.2 \
--data_dir=data/processed/cadec/ \
--train_input_file=train.txt \
--eval_input_file=dev.txt \
--test_input_file=test.txt \
--output_dir=outputs/cadec/ \
--do_train \
--find_best_decode_thresh \
--evaluate_during_training \
--train_max_seq_length=256 \
--eval_max_seq_length=512 \
--test_max_seq_length=512 \
--per_gpu_train_batch_size=16 \
--per_gpu_eval_batch_size=24 \
--per_gpu_test_batch_size=24 \
--gradient_accumulation_steps=1 \
--learning_rate=2e-5 \
--other_learning_rate=1e-3 \
--loss_type='ce' \
--weight_decay=0.0 \
--max_grad_norm=1.0 \
--scheduler_type=cosine \
--num_train_epochs=12 \
--checkpoint_mode=max \
--checkpoint_save_best \
--checkpoint_monitor=eval_f1_micro_all_entity \
--checkpoint_predict_code=checkpoint-eval_f1_micro_all_entity-best \
--classifier_dropout=0.6 \
--negative_sampling=0.0 \
--max_span_length=15 \
--width_embedding_size=128 \
--label_smoothing=0.0 \
--decode_thresh=0.5 \
--use_sinusoidal_width_embedding \
--do_lstm \
--num_lstm_layers=2 \
--do_biaffine_rel \
--use_last_n_layers=4 \
--warmup_proportion=0.1 \
--agg_last_n_layers=mean \
--do_gcn \
--gcn_hidden_size=128 \
--seed=42
```
```bash
python run_disc_ner.py \
--experiment_code=exp_share13_biobert_gcn_seed42 \
--task_name=share13 \
--model_type=bert \
--pretrained_model_path=./pretrained/Bio_ClinicalBERT \
--data_dir=data/processed/share13/ \
--train_input_file=train.txt \
--eval_input_file=dev.txt \
--test_input_file=test.txt \
--output_dir=outputs/share13/ \
--do_train \
--evaluate_during_training \
--train_max_seq_length=200 \
--eval_max_seq_length=512 \
--test_max_seq_length=512 \
--per_gpu_train_batch_size=8 \
--per_gpu_eval_batch_size=8 \
--per_gpu_test_batch_size=8 \
--gradient_accumulation_steps=2 \
--learning_rate=2e-5 \
--loss_type='ce' \
--other_learning_rate=1e-3 \
--weight_decay=0.01 \
--max_grad_norm=1.0 \
--scheduler_type=cosine \
--num_train_epochs=12 \
--checkpoint_mode=max \
--checkpoint_monitor=eval_f1_micro_all_entity \
--checkpoint_predict_code=checkpoint-eval_f1_micro_all_entity-best \
--classifier_dropout=0.5 \
--negative_sampling=0.0 \
--max_span_length=15 \
--width_embedding_size=128 \
--label_smoothing=0.0 \
--decode_thresh=0.5 \
--use_sinusoidal_width_embedding \
--do_lstm \
--num_lstm_layers=2 \
--do_biaffine_rel \
--use_last_n_layers=4 \
--warmup_proportion=0.1 \
--agg_last_n_layers=mean \
--do_gcn \
--gcn_hidden_size=128 \
--seed=42
```
```bash
python run_disc_ner.py \
--experiment_code=exp_share14_biobert_gcn_seed42 \
--task_name=share14 \
--model_type=bert \
--pretrained_model_path=./pretrained/Bio_ClinicalBERT \
--data_dir=data/processed/share14/ \
--train_input_file=train.txt \
--eval_input_file=dev.txt \
--test_input_file=test.txt \
--output_dir=outputs/share14/ \
--do_train \
--evaluate_during_training \
--train_max_seq_length=200 \
--eval_max_seq_length=512 \
--test_max_seq_length=512 \
--per_gpu_train_batch_size=8 \
--per_gpu_eval_batch_size=8 \
--per_gpu_test_batch_size=8 \
--gradient_accumulation_steps=2 \
--learning_rate=2e-5 \
--loss_type='ce' \
--other_learning_rate=1e-3 \
--weight_decay=0.01 \
--max_grad_norm=1.0 \
--scheduler_type=cosine \
--num_train_epochs=12 \
--checkpoint_mode=max \
--checkpoint_monitor=eval_f1_micro_all_entity \
--checkpoint_predict_code=checkpoint-eval_f1_micro_all_entity-best \
--classifier_dropout=0.5 \
--negative_sampling=0.0 \
--max_span_length=15 \
--width_embedding_size=128 \
--label_smoothing=0.0 \
--decode_thresh=0.5 \
--use_sinusoidal_width_embedding \
--do_lstm \
--num_lstm_layers=2 \
--do_biaffine_rel \
--use_last_n_layers=4 \
--warmup_proportion=0.1 \
--agg_last_n_layers=mean \
--do_gcn \
--gcn_hidden_size=128 \
--seed=42
```