Background The COVID-19 pandemic has led to an unprecedented amount of scientific
publications, growing at a pace never seen before. Multiple living systematic reviews have …

Named Entity Recognition (NER) in social media has received much research attention in
the field of natural language processing (NLP) and information extraction. Research on this …

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 …

Language models (LMs) pretrained on large corpora of text from the web have been
observed to contain large amounts of various types of knowledge about the world. This …

Due to the complexity of the biomedical domain, the ability to capture semantically
meaningful representations of terms in context is a long-standing challenge. Despite …

Current approaches for clinical information extraction are inefficient in terms of
computational costs and memory consumption, hindering their application to process large …

Computational literary uses data science and computer science techniques to study
literature. In this framework, we investigate how an expert system can acquire knowledge …

This paper outlines the performance evaluation of a system for adverse drug event
normalization, developed by the Data Science for Digital Health group for the Social Media …

Food recipe sharing sites are becoming increasingly popular among people who want to
learn how to cook or plan their menu. Through online food recipes, individuals can select …

The demand for accurate information extraction architectures for domain specific text data is
steadily increasing. Annotated target domain datasets are necessary for finetuning …