Background With the rapid adoption of electronic health records (EHRs), it is desirable to
harvest information and knowledge from EHRs to support automated systems at the point of …

We followed a systematic approach based on the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) …

An enormous amount of digital information is expressed as natural-language (NL) text that is
not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for …

Within the SemEval-2013 evaluation exercise, the TempEval-3 shared task aims to advance
research on temporal information processing. It follows on from TempEval-1 and-2, with: a …

Background Natural language processing applied to clinical text or aimed at a clinical
outcome has been thriving in recent years. This paper offers the first broad overview of …

Ontologies are at the core of the semantic web. As knowledge bases, they are very useful
resources for many artificial intelligence applications. Ontology learning, as a research area …

Part-of-speech information is a pre-requisite in many NLP algorithms. However, Twitter text
is difficult to part-of-speech tag: it is noisy, with linguistic errors and idiosyncratic style. We …

Electronic systems are increasingly present in the healthcare system and are often related to
improved medical care. However, the widespread use of these technologies ended up …

Electronic health record systems are ubiquitous and the majority of patients' data are now
being collected electronically in the form of free text. Deep learning has significantly …

Twitter is the largest source of microblog text, responsible for gigabytes of human discourse
every day. Processing microblog text is difficult: the genre is noisy, documents have little …