Extraction from the Web of Articles Describing Problems, Their Solutions, and Their Causes
Masaki Murata, Hiroki TANJI, Kazuhide Yamamoto, Stijn De Saeger, Yasunori Kakizawa, Kentaro Torisawa · IEICE Transactions on Information and Systems · 2011
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.