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Detecting causality from online psychiatric texts using inter-sentential language patterns.

Jheng-Long Wu1, Liang-Chih Yu, Pei-Chann Chang

  • 1College of Informatics, Department of Information Management, Yuan Ze University, Chung-Li, Taiwan, Republic of China.

BMC Medical Informatics and Decision Making
|July 20, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces inter-sentential language patterns to better detect cause-effect relationships in online psychiatric texts. This approach improves understanding of user problems for more effective online mental health support.

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Area of Science:

  • Computational Linguistics
  • Natural Language Processing
  • Mental Health Informatics

Background:

  • Online psychiatric texts from community web services contain user-expressed depressive problems.
  • Understanding cause-effect relations in these texts is crucial for improving online mental health services.

Purpose of the Study:

  • To develop a novel method for detecting causality in online psychiatric texts.
  • To overcome limitations of previous word-pair based methods by proposing inter-sentential language patterns.

Main Methods:

  • A text mining framework was developed by extending association rule mining.
  • This framework discovers frequently co-occurring patterns across sentence boundaries to form inter-sentential language patterns.
  • These patterns capture multi-word associations within and between sentences.

Main Results:

  • The proposed inter-sentential language patterns were evaluated on a corpus from PsychPark.
  • Experimental results demonstrated superior performance compared to traditional word-pair methods.
  • The new patterns provide more precise causal information.

Conclusions:

  • Inter-sentential language patterns are effective for causality detection in online psychiatric texts.
  • This method offers semantically richer features, enhancing the accuracy of causality detection.
  • The findings contribute to more effective analysis of online mental health data.