In the field of cancer genomics, the broad availability of genetic information offered by next-
generation sequencing technologies and rapid growth in biomedical publication has led to …

Motivation Annotation tools are applied to build training and test corpora, which are
essential for the development and evaluation of new natural language processing …

Objectives With the exponential increase in the number of articles published every year in
the biomedical domain, there is a need to build automated systems to extract unknown …

Named-entity recognition aims at identifying the fragments of text that mention entities of
interest, that afterwards could be linked to a knowledge base where those entities are …

Biomedical information extraction (BioIE) is important to many applications, including clinical
decision support, integrative biology, and pharmacovigilance, and therefore it has been an …

Background This article describes a high-recall, high-precision approach for the extraction of
biomedical entities from scientific articles. Method The approach uses a two-stage pipeline …

Recently, advanced text-mining techniques have been shown to speed up manual data
curation by providing human annotators with automated pre-annotations generated by rules …

Background We present a text-mining tool for recognizing biomedical entities in scientific
literature. OGER++ is a hybrid system for named entity recognition and concept recognition …

Biomedical open information extraction (BioOpenIE) is a novel paradigm to automatically
extract structured information from unstructured text with no or little supervision. It does not …

Text mining (TM) is a semi-automatized, multi-step process, able to turn unstructured into
structured data. TM relevance has increased upon machine learning (ML) and deep …