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• W3082330004 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." @default.
• W3082330004 created "2020-09-08" @default.
• W3082330004 creator A5005343110 @default.
• W3082330004 creator A5044244385 @default.
• W3082330004 creator A5057625030 @default.
• W3082330004 date "2020-08-28" @default.
• W3082330004 modified "2023-10-13" @default.
• W3082330004 title "Named Entity Recognition and Relation Detection for Biomedical Information Extraction" @default.
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