Named Entity Recognition and Relation Detection for Biomedical Information Extraction

Nadeesha Perera, Matthias Dehmer, Frank Emmert-Streib
<span title="2020-08-28">2020</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4dl6zyzmsrhczjurbzfc3hrzn4" style="color: black;">Frontiers in Cell and Developmental Biology</a> </i> &nbsp;
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
more &raquo; ... ctices 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fcell.2020.00673">doi:10.3389/fcell.2020.00673</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32984300">pmid:32984300</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7485218/">pmcid:PMC7485218</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/khclwjfykjfi3jktvrbuliwidm">fatcat:khclwjfykjfi3jktvrbuliwidm</a> </span>
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