The discovery paradigm of drugs is rapidly growing due to advances in machine learning
(ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug …

Motivation Biomedical named entity recognition (BioNER) seeks to automatically recognize
biomedical entities in natural language text, serving as a necessary foundation for …

This paper proposes a new natural language processing (NLP) application for identifying
medical jargon terms potentially difficult for patients to comprehend from electronic health …

The body of scientific literature continues to grow annually. Over 1.5 million abstracts of
biomedical publications were added to the PubMed database in 2021. Therefore …

Chemical Named Entity Recognition (NER) forms the basis of information extraction tasks in
the chemical domain. However, while such tasks can involve multiple domains of chemistry …

Text mining in the biomedical domain is increasingly important as the volume of biomedical
documents increases. Thanks to advances in natural language processing (NLP), extracting …

We study the problem of building entity tagging systems by using a few rules as weak
supervision. Previous methods mostly focus on disambiguation entity types based on …

Domain adaptation of Pretrained Language Models (PTLMs) is typically achieved by
unsupervised pretraining on target-domain text. While successful, this approach is …

Chemical patents represent a valuable source of information about new chemical
compounds, which is critical to the drug discovery process. Automated information extraction …

In the era of digital healthcare, the huge volumes of textual information generated every day
in hospitals constitute an essential but underused asset that could be exploited with task …