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Corpus-based statistical screening for phrase identification.

W Kim1, W J Wilbur

  • 1National Library of Medicine, Bethesda, Maryland 20894, USA. wonkim@ncbi.nlm.nih.gov

Journal of the American Medical Informatics Association : JAMIA
|September 14, 2000
PubMed
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Statistical methods effectively extract useful phrases from natural language databases. Combining six scoring techniques enhances phrase identification, enabling automatic hyperlink placement in medical texts.

Area of Science:

  • Natural Language Processing
  • Information Retrieval
  • Computational Linguistics

Background:

  • Extracting meaningful phrases from large text databases is crucial for information retrieval and indexing.
  • Leveraging human effort requires efficient methods for identifying high-quality phrases.

Purpose of the Study:

  • To develop and evaluate statistical methods for extracting useful phrases from natural language databases.
  • To improve the efficiency of preprocessing phrase lists for applications like indexing and hyperlink generation.

Main Methods:

  • Developed six distinct scoring methods based on statistical properties of word pairs and triples.
  • Utilized the Unified Medical Language System (UMLS) phrase list as a gold standard for validation.
  • Employed 11-point average precision and precision-recall curves to measure method effectiveness.

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Main Results:

  • All six statistical scoring methods effectively identified UMLS-quality phrases in a large MEDLINE subset.
  • Combined scoring methods demonstrated superior performance compared to individual methods.
  • The enhanced phrase extraction shows potential for automatic hyperlink placement in text.

Conclusions:

  • Statistical scoring methods offer a robust approach for extracting valuable phrases from natural language databases.
  • The developed methods are suitable for indexing and automatically generating hyperlinks.
  • This technique can significantly enhance the usability of large text corpora.