Extraction from the Web of Articles Describing Problems, Their Solutions, and Their Causes

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概要

• 論文の詳細を見る

• In this study, we extracted articles describing problems, articles describing their solutions, and articles describing their causes from a Japanese Q&A style Web forum using a supervised machine learning with 0.70, 0.86, and 0.56 F values, respectively. We confirmed that these values are significantly better than their baselines. This extraction will be useful to construct an application that can search for problems provided by users and display causes and potential solutions.

• 2011-03-01

著者

• Murata Masaki

  Tottori University

• Murata Masaki

  Tottori Univ. Tottori‐shi Jpn

• Torisawa Kentaro

  National Inst. Information And Communications Technol. Kyoto‐fu Jpn

• Torisawa Kentaro

  National Institute Of Information And Communications Technology

• TANJI Hiroki

  Nagaoka University of Technology

• YAMAMOTO Kazuhide

  Nagaoka University of Technology

• DE SAEGER

  National Institute of Information and Communications Technology

• KAKIZAWA Yasunori

  National Institute of Information and Communications Technology

関連論文

• Extraction from the Web of Articles Describing Problems, Their Solutions, and Their Causes

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