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Automated judgment of document qualities

  • Kwong Bor Ng
  • , Paul Kantor
  • , Tomek Strzalkowski
  • , Nina Wacholder
  • , Rong Tang
  • , Bing Bai
  • , Robert Rittman
  • , Peng Song
  • , Ying Sun
  • City University of New York
  • Rutgers - The State University of New Jersey, New Brunswick
  • SUNY Albany
  • Catholic University of America

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The authors report on a series of experiments to automate the assessment of document qualities such as depth and objectivity. The primary purpose is to develop a quality-sensitive functionality, orthogonal to relevance, to select documents for an interactive question-answering system. The study consisted of two stages. In the classifier construction stage, nine document qualities deemed Important by information professionals were identified and classifiers were developed to predict their values. In the confirmative evaluation stage, the performance of the developed methods was checked using a different document collection. The quality prediction methods worked well in the second stage. The results strongly suggest that the best way to predict document qualities automatically is to construct classifiers on a person-by-person basis.

Original languageEnglish
Pages (from-to)1155-1164
Number of pages10
JournalJournal of the American Society for Information Science and Technology
Volume57
Issue number9
DOIs
StatePublished - Jul 2006

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