Motivation The recognition of mentions of species names in text is a critically important task
for biomedical text mining. While deep learning-based methods have made great advances …

Community-run formal evaluations and manually annotated text corpora are critically
important for advancing biomedical text mining research. Recently in BioCreative V, a new …

Applying AI power to predict syntheses of novel materials requires high-quality, large-scale
datasets. Extraction of synthesis information from scientific publications is still challenging …

Abstract Background Semantic annotators and Natural Language Processing (NLP)
methods for Named Entity Recognition and Linking (NER+ L) require plenty of training and …

An estimated quarter of a trillion US dollars is invested in the biomedical research enterprise
annually. There is growing alarm that a significant portion of this investment is wasted …

Biomedical Named Entity Recognition (BioNER) is the fundamental task of identifying
named entities from biomedical text. However, BioNER suffers from severe data scarcity and …

Chemical named entity recognition (NER) is an active field of research in biomedical natural
language processing. To facilitate the development of new and superior chemical NER …

Background Biomedical named-entity recognition (BioNER) is widely modeled with
conditional random fields (CRF) by regarding it as a sequence labeling problem. The CRF …

Abstract The Precision Medicine Initiative is a multicenter effort aiming at formulating
personalized treatments leveraging on individual patient data (clinical, genome sequence …

Background The biomedical literature is growing rapidly, and it is increasingly important to
extract meaningful information from the vast amount of literature. Biomedical named entity …