-
Clone xMEN repository to obtain dataloaders and benchmark script:
git clone https://github.com/hpi-dhc/xmen cd xmen poetry install
-
Get latest version of SympTEMIST gazetteer from Zenodo (https://zenodo.org/records/10635215) and put the TSV into
xmen/local_files
-
Prepare KB and indicies for candidate generation:
xmen dict benchmarks/benchmark/symptemist.yaml --code examples/dicts/bsc_gazetteer.py xmen dict benchmarks/benchmark/symptemist.yaml --all
Notebook | Description |
---|---|
0_Dataset.ipynb | Statistics for SympTEMIST shared task and comparable datasets |
1_LLM_Simplification.ipynb | Applying LLM-based text simplification |
Executing 1_LLM_Simplification.ipynb
results in a dataset of candidates based on simplified mentions (symptemist_candidates_simplified_cutoff
) in the current folder. It can be used as a candidate set for running the full SympTEMIST entity linking pipeline with a trainable re-ranker.
The BERT checkpoint for initializing the cross-encoder can also be adapted.
cd xmen/benchmarks
python run_benchmark.py benchmark=symptemist output=./training
python run_benchmark.py benchmark=symptemist output=./training +candidates_path=../../symptemist/symptemist_candidates_simplified_cutoff
(e.g. for PlanTL-GOB-ES/roberta-base-bne
)
python run_benchmark.py benchmark=symptemist output=./training +candidates_path=../../symptemist/symptemist_candidates_simplified_cutoff linker.reranking.training.model_name=PlanTL-GOB-ES/roberta-base-bne