Abstract Biomedical Entity Linking (BEL) is the task of mapping of spans of text within
biomedical documents to normalized, unique identifiers within an ontology. This is an …

Motivation Biomedical entity linking (BEL) is the task of grounding entity mentions to a
knowledge base (KB). It plays a vital role in information extraction pipelines for the life …

Obtaining text datasets with semantic annotations is an effortful process, yet crucial for
supervised training in natural language processing (NLP). In general, developing and …

Despite remarkable advances in the development of language resources over the recent
years, there is still a shortage of annotated, publicly available corpora covering (German) …

Natural language processing (NLP) enables the extraction of information embedded within
unstructured texts, such as clinical case reports and trial eligibility criteria. By identifying …

Objective Healthcare data such as clinical notes are primarily recorded in an unstructured
manner. If adequately translated into structured data, they can be utilized for health …

We present a statistical model, GERNERMED++, for German medical natural language
processing trained for named entity recognition (NER) as an open, publicly available model …

The wide adoption of electronic health records (EHRs) offers immense potential as a source
of support for clinical research. However, previous studies focused on extracting only a …

We present CARDIO: DE, the first freely available and distributable large German clinical
corpus from the cardiovascular domain. CARDIO: DE encompasses 500 clinical routine …

In recent years, there has been a remarkable surge in the development of Natural Language
Processing (NLP) models, particularly in the realm of Named Entity Recognition (NER) …