Non-topical classification of healthcare information on the web

Sachio Hirokawa, Emi Ishita

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

The present paper collected the asthma related 4,762 Web pages from 1,759 sites using 6 queries. Each site is manually categorized by the standard topics of description and information dissemination, diary and idle talk and Q&A. By careful analysis, it turned out that the pages can be classified in non-topical categories such as 'reading level', 'objectivity/subjectivity' and 'reliability'. The manually assigned labels of non-topical categories are then used as learning data to apply SVM (support machine vector). The prediction performance (F-measure) were below 50% with the naive application of SVM. However, the prediction performance was improved over 50% by feature selection except for reading level.

本文言語英語
ホスト出版物のタイトルSmart Digital Futures 2014
出版社IOS Press
ページ237-247
ページ数11
ISBN(印刷版)9781614994046
DOI
出版ステータス出版済み - 1 1 2014

出版物シリーズ

名前Frontiers in Artificial Intelligence and Applications
262
ISSN(印刷版)0922-6389

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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