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Cimind: A phonetic-based tool for multilingual named entity recognition in biomedical texts.

Chloé Cabot1, Stéfan Darmoni2, Lina F Soualmia3

  • 1Normandie Univ., TIBS - LITIS EA 4108, Rouen Normandy University, France.

Journal of Biomedical Informatics
|April 14, 2019
PubMed
Summary

The Cimind system enhances multilingual medical Named Entity Recognition (NER) by using phonetic similarity to overcome spelling errors and resource limitations. It achieved high performance in the CLEF eHealth challenge for both English and French texts.

Keywords:
ControlledNamed Entity RecognitionNatural Language ProcessingVocabulary

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Biomedical text concept extraction is crucial for applications like information retrieval.
  • Clinical notes present Named Entity Recognition (NER) challenges including spelling errors, grammatical issues, and non-standard abbreviations.
  • Limited English-centric resources hinder multilingual NER in many countries.

Purpose of the Study:

  • To present Cimind, a multilingual system for medical Named Entity Recognition (NER).
  • To address challenges in clinical note NER, particularly spelling errors and language barriers.
  • To leverage phonetic similarity for improved entity recognition in diverse medical texts.

Main Methods:

  • Cimind combines phonetic recognition (DM algorithm) with string similarity measures for entity recognition.
  • The system employs a three-step process: normalization, phonetic similarity-based candidate selection, and candidate ranking.
  • It identifies terms within a controlled vocabulary.

Main Results:

  • Cimind demonstrated strong performance in the 2017 CLEF eHealth challenge.
  • Results included 81.0% F1 for English, 76.4% F1 for French raw data, and 80.4% F1 for French aligned data.
  • The system ranked first in French and fourth in English in official runs.

Conclusions:

  • Cimind effectively handles spelling errors and multilingual challenges in medical NER.
  • The system's phonetic similarity approach is valuable for processing noisy clinical text.
  • Cimind shows competitive performance across different languages in biomedical Named Entity Recognition.