BioWordVec & BioSentVec: pre-trained embeddings for biomedical words and sentences
BLUE benchmark consists of five different biomedicine text-mining tasks with ten corpora.
Multimodal Question Answering in the Medical Domain: A summary of Existing Datasets and Systems
Browse Covid-19 & SARS-CoV-2 Scientific Papers with Transformers 🦠 📖
[NAACL'21 & ACL'21] SapBERT: Self-alignment pretraining for BERT & XL-BEL: Cross-Lingual Biomedical Entity Linking.
A large-scale (194k), Multiple-Choice Question Answering (MCQA) dataset designed to address realworld medical entrance exam questions.
A Python biomedical relation extraction package that uses a supervised approach (i.e. needs training data).
NLP framework in python for entity recognition and relationship extraction
Challenge on Textual Inference and Question Entailment in the Medical Domain https://sites.google.com/view/mediqa2019
Corpus of Online Medical EnTities: the cometA corpus
Named Entity Recognition for biomedical entities
A Causal Relation Schema for Text
A framework for keeping biomedical text mining result up-to-date
Text-mined knowledgebase for drivers, oncogenes and tumor suppressors in cancer
Tokenization, sentence segmentation, POS tagging and dependency parsing for biomedical texts (BMC Bioinformatics 2019)
Repository used to collect biomedical corpus on the Internet!
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