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Defining a state-of-the-art POS-tagging environment for Brazilian Portuguese clinical texts

Abstract

Purpose

Natural language processing techniques are essential for unlocking patients’ data from electronic health records. An important NLP task is the ability to recognize morphosyntactic information from the texts, a process called part-of-speech (POS) tagging. Currently, neural network architectures are the state-of-the-art method, although there is a lack of studies exploiting this approach within Brazilian Portuguese clinical texts. The objective of this study is to define a state-of-the-art POS-tagging environment for Brazilian Portuguese clinical texts.

Methods

We reviewed multiple neural network-based POS-tagging algorithms, and the Flair tool was selected due to its exceptional performance in the journalistic domain, as there is any specific algorithm to Portuguese clinical texts. We executed a normalization process on available corpora from multiple domains (two journalistic, one biomedical, one clinical, and a new corpus composed of all three of these). The Flair algorithm was trained with all corpora, generating five models, which were evaluated with all domains.

Results

The clinical model achieved 92.39% accuracy (previous POS-tagging clinical work reached 91.5%); the biomedical model achieved 97.9% accuracy. All the models were assessed on their own test set.

Conclusion

We developed a new state-of-the-art modeling environment for POS tagging of Brazilian Portuguese clinical texts and achieved comparable results to other state-of-the-art studies in journalistic contexts.

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Notes

  1. 1.

    https://catalog.ldc.upenn.edu/LDC99T42

  2. 2.

    http://opennlp.sourceforge.net

  3. 3.

    https://github.com/flairNLP/flair

  4. 4.

    https://github.com/HAILab-PUCPR/pos-tagging-tagset-normalization

  5. 5.

    https://github.com/flairNLP/flair/blob/master/resources/docs/TUTORIAL_4_ELMO_BERT_FLAIR_EMBEDDING.md

  6. 6.

    https://colab.research.google.com/

  7. 7.

    https://github.com/HAILab-PUCPR/portuguese-clinical-pos-tagger

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Funding

We would like to thank the Philips Research North America and Fundação Araucária for financing this research.

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Correspondence to Lucas Emanuel Silva e Oliveira.

Ethics declarations

The UNI, MAC and BOS corpora were freely available for download since they do not make use of sensitive data. The PUC clinical corpus has already been anonymized and approved for research use in its original project (Peters et al., 2010), through protocol number 0375.084.000-10 of the National Commission for Research Ethics, and from the Research Ethics Committee of the Pontifical Catholic University of Paraná (0004422/10).

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The authors declare that they have no conflict of interest.

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de Oliveira, L.F.A., e Oliveira, L.E.S., Gumiel, Y.B. et al. Defining a state-of-the-art POS-tagging environment for Brazilian Portuguese clinical texts. Res. Biomed. Eng. 36, 267–276 (2020). https://doi.org/10.1007/s42600-020-00067-7

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Keywords

  • Natural language processing
  • POS tagging
  • Clinical narratives
  • Neural networks