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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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Published on: February 23, 2019

Exploring subdomain variation in biomedical language.

Thomas Lippincott1, Diarmuid Ó Séaghdha, Anna Korhonen

  • 1Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, UK. Thomas.Lippincott@cl.cam.ac.uk

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Summary

Natural Language Processing (NLP) in biomedicine shows linguistic variation across subdomains. Genetics and molecular biology texts, often used for NLP training, do not represent all biomedical fields.

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Area of Science:

  • Biomedical Informatics
  • Computational Linguistics

Background:

  • Natural Language Processing (NLP) applications in biomedical texts are rapidly growing.
  • Linguistic variation across different biomedical subdomains is not well-understood.
  • Existing NLP research often overlooks fine-grained linguistic differences within biomedicine.

Purpose of the Study:

  • To investigate linguistic subdomain variation within the biomedical domain.
  • To determine the extent to which different biomedical subject areas exhibit distinct linguistic behaviors.
  • To assess the impact of this variation on Natural Language Processing (NLP) system performance.

Main Methods:

  • Utilized the OpenPMC text corpus, covering diverse biomedical subdomains.
  • Analyzed variation across lexical, syntactic, semantic, and discourse features.
  • Employed clustering techniques to identify commonalities and distinctions among subdomains.

Main Results:

  • Identified robust clusters of subdomains based on linguistic features.
  • Observed distinct linguistic patterns, notably separating clinical and laboratory-oriented subjects.
  • Found that genetics and molecular biology subdomains are not representative of the broader biomedical landscape.

Conclusions:

  • Awareness of linguistic subdomain variation is crucial for effective biomedical NLP.
  • The common practice of using genetics/molecular biology data may limit the generalizability of NLP tools.
  • Further research into domain-specific NLP adaptation is warranted for biomedical applications.