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Named Entity Recognition and Relation Detection for Biomedical Information Extraction.

Nadeesha Perera1, Matthias Dehmer2,3, Frank Emmert-Streib1,4

  • 1Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.

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Summary

Scientific literature is growing, but valuable data is locked in text. Natural language processing (NLP) and text mining, including Named Entity Recognition (NER) and Relation Detection (RD), extract this information for analysis.

Keywords:
artificial intelligencedeep learninginformation extractionnamed entity recognitionnatural language processingrelation detectiontext analyticstext mining

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Area of Science:

  • Biomedical and Health Informatics
  • Clinical Sciences
  • Computational Biology

Background:

  • The exponential growth of scientific publications presents challenges for data accessibility and utilization.
  • Vast amounts of critical information remain embedded within unstructured text, hindering analysis and knowledge discovery.
  • Current methods lack automated systems for archiving and extracting key findings from research articles.

Purpose of the Study:

  • To review current practices in Named Entity Recognition (NER) and Relation Detection (RD) for information extraction from scientific literature.
  • To explore the application of these methods in identifying biological and clinical relationships, such as drug-protein interactions and gene-disease associations.
  • To survey emerging deep learning techniques applied to these natural language processing (NLP) tasks.

Main Methods:

  • Review of existing literature on Named Entity Recognition (NER) and Relation Detection (RD) techniques.
  • Analysis of NLP and text mining approaches for extracting structured data from biomedical texts.
  • Survey of recent advancements in deep learning for information extraction tasks.

Main Results:

  • Identified key practices in NER and RD for extracting entities like genes, proteins, drugs, and diseases.
  • Demonstrated how extracted information can be integrated into networks for data management and analysis.
  • Highlighted the growing role of deep learning in improving the accuracy and efficiency of information extraction.

Conclusions:

  • NLP and text mining are crucial for unlocking the wealth of information in scientific publications.
  • NER and RD methods enable the creation of structured knowledge bases from unstructured text.
  • Deep learning offers promising avenues for advancing information extraction in biomedical and clinical sciences.