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TermInformer: unsupervised term mining and analysis in biomedical literature.

Prayag Tiwari1, Sagar Uprety2, Shahram Dehdashti3

  • 1Department of Information Engineering, University of Padova, Padua, Italy.

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Summary

This study introduces TermInformer, an unsupervised method for automatically mining biomedical terms and their relationships from text. This approach enhances natural language processing (NLP) applications in the biomedical field.

Keywords:
Biomedical literatureGloVeSequence labellingTerm embeddingsTerm miningUnsupervised learning

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Accurate terminology mining is crucial for biomedical research and literature analysis.
  • Current methods like named entity recognition and dictionary-based approaches have limitations in unsupervised biomedical text mining.
  • Existing methods often require large labeled corpora or extensive manual effort.

Purpose of the Study:

  • To propose an unsupervised method for automatic term mining in biomedical literature.
  • To develop a system (TermInformer) that uncovers semantic relationships between terms without external resources.
  • To generate reusable term embeddings for downstream natural language processing (NLP) tasks.

Main Methods:

  • Developed an unsupervised term mining approach applicable to any document data.
  • Integrated word vector training to create reusable term embeddings.
  • Compared the proposed term embeddings with existing word embeddings.

Main Results:

  • The proposed method effectively mines terms and their semantic relationships from biomedical text.
  • Term embeddings generated by this method demonstrate superior reflection of semantic relationships compared to existing word embeddings.
  • The method was successfully applied to identify potential factors and treatments for lung cancer, breast cancer, and coronavirus.

Conclusions:

  • The TermInformer project offers a novel unsupervised solution for biomedical term mining.
  • The generated term embeddings can significantly benefit various NLP applications in the biomedical domain.
  • This approach facilitates the discovery of insights into complex diseases and potential therapeutic strategies.