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Semantic classification of Indonesian consumer health questions.

Journal of biomedical semantics·2025
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Sentences, entities, and keyphrases extraction from consumer health forums using multi-task learning.

Tsaqif Naufal1, Rahmad Mahendra1, Alfan Farizki Wicaksono2

  • 1Faculty of Computer Science, Universitas Indonesia, Kampus UI, 16424, Depok, West Java, Indonesia.

Journal of Biomedical Semantics
|May 6, 2025
PubMed
Summary

Developing natural language processing tools for health forums improves question understanding. Multi-task learning enhances medical entity recognition and keyphrase extraction, aiding users in finding health information more effectively.

Keywords:
Consumer health question-answering systemKeyphrase extractionMedical entity recognitionMulti-task learningSentence recognition

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Area of Science:

  • Natural Language Processing
  • Health Informatics
  • Computational Linguistics

Background:

  • Online health forums are valuable information sources but face challenges with professional engagement.
  • Limited healthcare professional participation can delay responses to user health queries.
  • Semi-automatic systems with advanced question processing can enhance forum effectiveness.

Purpose of the Study:

  • To develop and evaluate natural language processing (NLP) modules for health-related question processing.
  • To improve the identification of critical components within user questions for better understanding.
  • To enable the re-formulation of more effective questions using extracted key information.

Main Methods:

  • Expansion and public release of Indonesian datasets for sentence recognition (SR), medical entity recognition (MER), and keyphrase extraction (KE).
  • Establishment of baselines using transformer-based models with nine encoder variations for Indonesian language.
  • Proposal and evaluation of multi-task learning (MTL) models in pairwise and three-way configurations with parallel and hierarchical architectures.

Main Results:

  • Inter-annotator agreements for SR, MER, and KE tasks were established.
  • Single-task learning (STL) showed varying best-performing models, indicating larger models are not always superior.
  • Pairwise MTL models outperformed STL baselines for all three tasks.
  • Three-way MTL models showed inconsistent performance patterns, with some configurations improving MER and KE but not SR.

Conclusions:

  • An extended Indonesian dataset for SR, MER, and KE tasks was created.
  • Transformer-based models established baselines for these NLP tasks in Indonesian.
  • MTL approaches demonstrated that shared information benefits MER and KE learning more than SR.