Pre-trained language models (PLMs) have been the de facto paradigm for most natural
language processing tasks. This also benefits the biomedical domain: researchers from …

Transformer-based pretrained language models (PLMs) have started a new era in modern
natural language processing (NLP). These models combine the power of transformers …

The utilization of large language models (LLMs) in the Healthcare domain has generated
both excitement and concern due to their ability to effectively respond to freetext queries with …

Background: Knowledge is evolving over time, often as a result of new discoveries or
changes in the adopted methods of reasoning. Also, new facts or evidence may become …

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 …

Clinical data in hospitals are increasingly accessible for research through clinical data
warehouses, however these documents are unstructured. It is therefore necessary to extract …

Objectives: This survey aims to provide an overview of the current state of biomedical and
clinical Natural Language Processing (NLP) research and practice in Languages other than …

L'édition 2020 du défi fouille de texte (DEFT) a proposé deux tâches autour de la similarité
textuelle et une tâche d'extraction d'information. La première tâche vise à identifier le degré …

Over the past few years, domain specific pretrained language models have been
investigated and have shown remarkable achievements in different downstream tasks …