An Extensible Classifier for Semi-Structured Documents.

@inproceedings{DBLP:conf/cikm/TreschL95,
  author    = {Markus Tresch and
               Allen Luniewski},
  title     = {An Extensible Classifier for Semi-Structured Documents},
  booktitle = {CIKM '95, Proceedings of the 1995 International Conference on
               Information and Knowledge Management, November 28 - December
               2, 1995, Baltimore, Maryland, USA},
  publisher = {ACM},
  year      = {1995},
  pages     = {226-233},
  ee        = {db/conf/cikm/TreschL95.html, http://doi.acm.org/10.1145/221270.221575},
  crossref  = {DBLP:conf/cikm/95},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

In this paper, we present a 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 flexibility.

The ability to dynamically extend a classifier with user-specific classes is crucial for many applications. Unfortunately, the training data of existing classes is often not available, such that the extended classifier is imprecise as a result.

We focus on this issue. First, we evaluate how to create class abstracts that can be used as training data replacement. Second, we introduce relevance feedback learning strategies to overcoming the remaining classifier flaw.

Copyright © 1995 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.

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