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Lable_enhanced_NER/lable_enhanced_ner	A label-enhanced model for biomedical named entity recogniton (NER)	2 years ago

README.md	update	1 year ago

README.md

1. Labele_enhanced_NER
2. ADR实体识别及标准化

1. Labele_enhanced_NER

1.1 Environment

python:3.5
tensorflow: 1.11

MRC for NER

use machine reading comprehension (MRC) model to solve NER task.
each data is a tuple (question,passage,start_pisition,end_position)
In NER, question is the lable definition for each entity type, passage is each sentence, start_position is the start position of each entity
and end_position is the end position of each entity.

Single One-pass Model (SOne) for NER

use single one-pass model to solve NER task.
Each data ia a tuple (passage, start_position1, end_position1, start_position2, end_position2, ...)

1.2 RUN

bash run_mrc.sh
run SOne-based model
bash run_sone.sh

1.3 decode

get the answer of submit file format
python trans2answer.py

2. ADR实体识别及标准化

2.1 Environment

python 3.6

TensorFlow 1.12

2.2 使用说明

1.run_bykt_ner_sac_cadec.sh为运行基于序列标注的药物副作用实体识别
2.run_ner_qa.sh为运行基于阅读理解的药物副作用实体识别
3.run_multicalss_bykt_cadec.sh为运行基于多分类的实体标准化
4.run_rank_bykt_cadec.sh为运行基于排序的实体标准化

2.3 Result

** Psytar数据集**

NER结果（SAC lambda=0.1）

model	P	R	F	运行路径
BiLSTM+CRF	0.6489	0.7007	0.6738	/home/cs/NCRF-SAC/model/NCRFSAC/
BERT+CRF	0.6454	0.7913	0.7170	/home/cs/bert/run_bykt_ner_psytar.sh
BiLSTM+CRF+SAC	0.6598	0.7072	0.6826	/home/cs/NCRF-SAC/model/NCRFSAC/predict_bykt_v2.sh
BERT+CRF+SAC（0.1）	0.6533	0.8136	0.7247	/home/cs/bert/run_bykt_ner_sac_psytar.sh
BiLSTM+CRF+SAC+Drug_Emb	0.6375	0.7425	0.6860	/home/cs/NCRF-SAC/model/NCRFSAC/predict_bykt_v3.sh
BERT+CRF+SAC+Drug_Emb	0.6592	0.8123	0.7278	/home/cs/bert/run_bykt_ner_sac_de_psytar.sh
BERT+MRC	0.7427	0.7320	0.7373	/home/cs/bert/run_ner_qa.sh

** Cadec数据集 **

NER结果（SAC lambda=0.1）

model	P	R	F	运行路径
BiLSTM+CRF	0.6399	0.6909	0.6645	predict_bykt_v4.sh
BERT+CRF	0.6555	0.7192	0.6858	run_bykt_ner_cadec.sh
BiLSTM+CRF+SAC	0.6647	0.6791	0.6719	predict_bykt_v5.sh
BERT+CRF+SAC（0.1）	0.6519	0.7417	0.6939	run_bykt_ner_sac_cadec.sh
BiLSTM+CRF+SAC+Drug_Emb	0.6685	0.6988	0.6833	predict_bykt_v6.sh
BERT+CRF+SAC+Drug_Emb	0.6527	0.7561	0.7006	run_bykt_ner_sac_de_cadec.sh
BERT+MRC	0.7415	0.6870	0.7132	run_ner_qa.sh