Named Entity Recognition and Relation Detection for Biomedical Information Extraction

Nadeesha Perera; Matthias Dehmer; Frank Emmert-Streib

doi:10.3389/fcell.2020.00673

Named Entity Recognition and Relation Detection for Biomedical Information Extraction

Front Cell Dev Biol. 2020 Aug 28:8:673. doi: 10.3389/fcell.2020.00673. eCollection 2020.

Authors

Nadeesha Perera¹, Matthias Dehmer^{2

3}, Frank Emmert-Streib^{1

4}

Affiliations

¹ Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.
² Department of Mechatronics and Biomedical Computer Science, University for Health Sciences, Medical Informatics and Technology (UMIT), Hall in Tirol, Austria.
³ College of Artificial Intelligence, Nankai University, Tianjin, China.
⁴ Faculty of Medicine and Health Technology, Institute of Biosciences and Medical Technology, Tampere University, Tampere, Finland.

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.

Keywords: artificial intelligence; deep learning; information extraction; named entity recognition; natural language processing; relation detection; text analytics; text mining.

Publication types

Review