Named Entity Recognition and Relation Detection for Biomedical Information Extraction
Research output: Contribution to journal › Review Article › Scientific › peer-review
|Number of pages||26|
|Journal||Frontiers in cell and developmental biology|
|Publication status||Published - 28 Aug 2020|
|Publication type||A2 Review article in a scientific journal|
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.
- artificial intelligence, deep learning, information extraction, named entity recognition, natural language processing, relation detection, text analytics, text mining