Named Entity Recognition and Relation Detection for Biomedical Information Extraction

Overview

Journal Front Cell Dev Biol

Specialty Cell Biology

Date 2020 Sep 28

PMID 32984300

Citations 33

Authors

Nadeesha Perera

Matthias Dehmer

Frank Emmert-Streib

Affiliations

Soon will be listed here.

Abstract

The number of scientific publications in the literature is steadily growing, containing our knowledge in the biomedical, health, and clinical sciences. Since there is currently no automatic archiving of the obtained results, much of this information remains buried in textual details not readily available for further usage or analysis. For this reason, natural language processing (NLP) and text mining methods are used for information extraction from such publications. In this paper, we review practices for Named Entity Recognition (NER) and Relation Detection (RD), allowing, e.g., to identify interactions between proteins and drugs or genes and diseases. This information can be integrated into networks to summarize large-scale details on a particular biomedical or clinical problem, which is then amenable for easy data management and further analysis. Furthermore, we survey novel deep learning methods that have recently been introduced for such tasks.

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