Background Chemical and biomedical named entity recognition (NER) is an essential
preprocessing task in natural language processing. The identification and extraction of …

Background Chemical entity recognition has traditionally been performed by machine
learning approaches. Here we describe an approach using grammars and dictionaries. This …

There is a pressing need for the automated extraction of chemical reaction information
because of the rapid growth of scientific documents. The previously reported works in the …

It is getting increasingly challenging to efficiently exploit drug-related information described
in the growing amount of scientific literature. Indeed, for drug–gene/protein interactions, the …

The increasing amount of publicly available research data provides the opportunity to link
and integrate data in order to create and prove novel hypotheses, to repeat experiments or …

Named-entity recognition aims at identifying the fragments of text that mention entities of
interest, that afterwards could be linked to a knowledge base where those entities are …

The massive increase in materials science publications has resulted in a bottleneck in
efficiently connecting new materials discoveries to knowledge from established literature …

Combining Domain-adaptive Pre-training (DAPT) with Federated Learning (FL) can
enhance model adaptation by leveraging more sensitive and distributed data while …

Rare diseases affect a small number of people compared to the general population.
However, more than 6,000 different rare diseases exist and, in total, they affect more than …

The article presents a workflow to create a question-answering system whose knowledge
base combines knowledge graphs and scientific publications on coronaviruses. It is based …