KaNER

code for TCBB paper "Knowledge Adaptive Multi-way Matching Network for Biomedical Named Entity Recognition via Machine Reading Comprehension"

Examples Instructions

(1) Train BioNLP11EPI on train dataset, evaluate on dev dataset:

python ./BioNER.py train --config configs/train.conf

(2) Evaluate the BioNLP11EPI model on test dataset:

python ./BioNER.py eval --config configs/eval.conf

Fetch data

datasets lies in data file

Additional:

• The files Knowledge_Vocabulary.json, Concept description information.txt can be extracted directly from UMLS by MetaMap tool(to use UMLS, you need to request access permission). Note that some UMLS concepts may not have any definition sentence.
• Knowledge_Embeddings.npy contains the UMLS embeddings of Maldonado et al. 2019.
• scispaCy preprocess the concept description information

References

[1] Eberts, Markus, and Adrian Ulges. "Span-based joint entity and relation extraction with transformer pre-training." arXiv preprint arXiv:1909.07755 (2019).
[2] Li, Xiaoya, et al. "A unified MRC framework for named entity recognition." arXiv preprint arXiv:1910.11476 (2019).