Overview

- S1-EBD（Entity Boundary Detaction Module
    | - biobertNER (code for Flat Supervised NER)
    | - dsner (code for DSNER NER)
    | - uni (code for unified NER)

- S2-BEM (Biomedical Entity Matching Module)
    | - dictionary 
    | - script (script to run the BEM and DR)
    | - testdata (Stores the output data from the EBD model)
    | - output（Output results from evaluation scripts for ensemble results）

Supervised NER

Downloading huggingface biobert-v1.1 embeding into S1-EBD/embed

Our flat NER module is based on biobert-pytorch, so the requirements should be consistent with that project.

You can download the biomedical NER dataset following this link

(1) EBD

export DMNER_ROOT=/home/test2/DMNER
cd $DMNER_ROOT/DMNER/S1-EBD/biobertNER/NER 
sh train_ncbi.sh $GPUID
sh infer_ncbi.sh $GPUID

(2): BEM

Get init dictionary

export DMNER_ROOT=/home/test2/DMNER
cd $DMNER_ROOT/S2-BEM/script
python dict_init_fromgold.py --dname NCBI --etype Disease --droot ${DMNER_ROOT}

Dictionary refinement & Ensemble the results

python all.py --dname NCBI --gpu $GPUID

Distantly Supervised NER

The EBD backbone of DS-NER is borrowed from MRC. The environment needs to be reconfigured.

The trusted entities and unknown entities used in training come from autoner.

(1) EBD

export DMNER_ROOT=/home/test2/DMNER
cd $DMNER_ROOT/S1-EBD/dsner
sh paral_train_bc5cdr.sh $GPUID
sh infer_bc5cdr.sh $GPUID

(2) BEM Dictionary refinement & Ensemble the results

python all.py --dname BC5CDR --gpu $GPUID

Unified NER

(1) EBD

export DMNER_ROOT=/home/test2/DMNER
cd $DMNER_ROOT/S1-EBD/uni
sh paral_train_uni.sh $GPUID
sh infer_bc5cdr.sh $GPUID

(2) BEM Dictionary refinement & Ensemble the results

python all.py --dname BC5CDR-UNI --gpu $GPUID