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 …

Negation and speculation are universal linguistic phenomena that affect the performance of
Natural Language Processing (NLP) applications, such as those for opinion mining and …

In recent years, pre-trained language models (PLMs) achieve the best performance on a
wide range of natural language processing (NLP) tasks. While the first models were trained …

This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice
Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 …

Background The increasing complexity of data streams and computational processes in
modern clinical health information systems makes reproducibility challenging. Clinical …

In a number of circumstances, obtaining health-related information from a patient is time-
consuming, whereas a chatbot interacting efficiently with that patient might help saving …

In sensitive domains, the sharing of corpora is restricted due to confidentiality, copyrights, or
trade secrets. Automatic text generation can help alleviate these issues by producing …

Automatic detection of negated content is often a prerequisite in information extraction
systems in various domains. In the biomedical domain especially, this task is important …

Named entity recognition (NER) is key for biomedical applications as it allows knowledge
discovery in free text data. As entities are semantic phrases, their meaning is conditioned to …

Pre-trained transformer language models (LMs) have in recent years become the dominant
paradigm in applied NLP. These models have achieved state-of-the-art performance on …