Type Classification of Semi-Structured Documents.

@inproceedings{DBLP:conf/vldb/TreschPL95,
  author    = {Markus Tresch and
               Neal Palmer and
               Allen Luniewski},
  editor    = {Umeshwar Dayal and
               Peter M. D. Gray and
               Shojiro Nishio},
  title     = {Type Classification of Semi-Structured Documents},
  booktitle = {VLDB'95, Proceedings of 21th International Conference on Very
               Large Data Bases, September 11-15, 1995, Zurich, Switzerland},
  publisher = {Morgan Kaufmann},
  year      = {1995},
  isbn      = {1-55860-379-4},
  pages     = {263-274},
  ee        = {db/conf/vldb/TreschPL95.html},
  crossref  = {DBLP:conf/vldb/95},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Semi-structured documents (e.g. journal articles, electronic mail, television programs, mail order catalogs, ...) are often not explicitly typed; the only available type information is the implicit structure. An explicit type, however, is needed in order to apply object- oriented technology, like type-specific methods.

In this paper, we present an experimental vector space classifier for determining the type of semi-structured documents. Our goal was to design a high-performance classifier in terms of accuracy (recall and precision), speed, and extensibility.

Copyright © 1995 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

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Referenced by

1. Serge Abiteboul: Querying Semi-Structured Data. ICDT 1997: 1-18
2. Markus Tresch, Allen Luniewski: An Extensible Classifier for Semi-Structured Documents. CIKM 1995: 226-233