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The HoneyComb Paradigm for Research on Collective Human Behavior
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Extraction of a group-pair relation: problem-solving relation from web-board documents.

Chaveevan Pechsiri1, Rapepun Piriyakul2

  • 1Department of Information Technology, DhurakijPundit University, Bangkok, Thailand.

Springerplus
|August 20, 2016
PubMed
Summary

This study extracts problem-solving relations, like symptom-treatment, from web data. The novel approach accurately identifies and links problem and solution concepts for better public understanding.

Keywords:
Elementary discourse unitProblem-solving relationSemantic relationWord co-occurrence

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Area of Science:

  • Natural Language Processing
  • Information Extraction
  • Computational Linguistics

Background:

  • Extracting problem-solving relations from unstructured text is challenging.
  • Existing methods lack accuracy in identifying complex event-explanation relationships.
  • Non-professional users need accessible knowledge for problem resolution.

Purpose of the Study:

  • To develop a method for extracting Problem-Solving relations between problem and solution concepts.
  • To accurately identify and define boundaries for problem-concept and solving-concept Elementary Discourse Units (EDUs).
  • To enhance public understanding of problem-solving through accessible knowledge representation.

Main Methods:

  • Utilized word co-occurrence to identify problem-concept and solving-concept EDUs.
  • Applied machine learning techniques for determining EDU boundaries.
  • Employed k-mean clustering and Naïve Bayes for classifying Problem-Solving relations between event-explanation groups.

Main Results:

  • Achieved high accuracy in group-pair relation extraction.
  • Successfully identified and delineated problem and solution EDUs from web board documents.
  • Demonstrated the effectiveness of clustering features for relation determination.

Conclusions:

  • The proposed approach accurately extracts Problem-Solving relations, specifically Symptom-Treatment and CarProblem-Repair.
  • This method facilitates knowledge discovery for non-experts by providing clear problem-solving pathways.
  • The research advances information extraction techniques for real-world applications.