A model for evaluating the quality of user-created documents

Linh Hoang, Jung Tae Lee, Young In Song, Hae Chang Rim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

In this paper, we propose a model for evaluating the quality of general user-created documents. The model is based on supervised classification approach, in which output scores are considered as quality of given document. In order to utilize both textual and non-textual attributes of documents, we incorporated a number of objectively measurable, real-valued features selected upon predefined criteria for quality. Experiments on two datasets of real world documents show that textual features are stable indicators for evaluating documents' quality. Some features are inferred to be effective for general kinds of documents.

Original languageEnglish
Title of host publicationInformation Retrieval Technology - 4th Asia Information Retrieval Symposium, AIRS 2008, Revised Selected Papers
Pages496-501
Number of pages6
DOIs
Publication statusPublished - 2008
Event4th Asia Information Retrieval Symposium, AIRS 2008 - Harbin, China
Duration: 2008 Jan 152008 Jan 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4993 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th Asia Information Retrieval Symposium, AIRS 2008
CountryChina
CityHarbin
Period08/1/1508/1/18

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'A model for evaluating the quality of user-created documents'. Together they form a unique fingerprint.

  • Cite this

    Hoang, L., Lee, J. T., Song, Y. I., & Rim, H. C. (2008). A model for evaluating the quality of user-created documents. In Information Retrieval Technology - 4th Asia Information Retrieval Symposium, AIRS 2008, Revised Selected Papers (pp. 496-501). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4993 LNCS). https://doi.org/10.1007/978-3-540-68636-1_54