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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
Publication
IEICE TRANSACTIONS on Information and Systems
Vol.E94-D
No.3
pp.734-737 Publication Date: 2011/03/01 Online ISSN: 1745-1361
DOI: 10.1587/transinf.E94.D.734 Print ISSN: 0916-8532 Type of Manuscript: LETTER Category: Natural Language Processing Keyword: problems, solutions, causes, Web, learning,
Full Text: PDF(66.5KB)>>
Summary:
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.
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