Autonomous Document Classification for Business

by

Clack, C., Farringdon, J., Lidwell, P. and Yu, T.

Literature search on Evolutionary ComputationBBase ©1999-2013, Rasmus K. Ursem
     Home · Search · Adv. search · Authors · Login · Add entries   Webmaster
Note to authors: Please submit your bibliography and contact information - online papers are more frequently cited.

Info: The First International Conference on Autonomous Agents (Agents '97) (Conference proceedings), 1997, p. 201-208
Keywords:genetic algorithms, genetic programming
Abstract:
With the continuing exponential growth of the Internet and the more recent growth of business Intranets, the commercial world is becoming increasingly aware of the problem of electronic information overload. This has encouraged interest in developing agents/softbots that can act as electronic personal assistants and can develop and adapt representations of users information needs, commonly known as profiles. As the result of collaborative research with Friends of the Earth, an environmental issues campaigning organisation, we have developed a general purpose information classification agent architecture [AA] and have applied it to the problem of document classification and routing. Collaboration with Friends of the Earth allows us to test our ideas in a non-academic context involving high volumes of documents. We use the technique of genetic programming (GP), [GP] (Koza and Rice 1992), to evolve classifying agents. This is a novel approach for document classification, where each agent evolves a parse-tree representation of a user's particular information need. The other unusual features of our research are the longevity of our agents and the fact that they undergo a continual training process; feedback from the user enables the agent to adapt to the user's long-term information requirements.
Notes:
http://www.isi.edu/isd/AA97/info.html see also clack:1996:adcb
URL(s):(G)zipped postscript
HTML

Review item:

Mark as doublet (will be reviewed)

Print entry



BibTex:
@InProceedings{clack:1997:adcb,
  author =       "Chris Clack and Jonny Farringdon and Peter Lidwell and
                 Tina Yu",
  title =        "Autonomous Document Classification for Business",
  booktitle =    "The First International Conference on Autonomous
                 Agents (Agents '97)",
  year =         "1997",
  editor =       "W. Lewis Johnson",
  pages =        "201--208",
  address =      "Marina del Rey, California, USA",
  publisher_address = "1515 Broadway, New York, NY 10036, USA",
  month =        feb # " 5-8",
  organisation = "ACM SIGART",
  publisher =    "ACM Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-89791-877-0",
  URL =          "http://www.cs.ucl.ac.uk/staff/J.Farringdon/GP/Papers-agent97/agent97.html",
  size =         "11 pages",
  abstract =     "With the continuing exponential growth of the Internet
                 and the more recent growth of business Intranets, the
                 commercial world is becoming increasingly aware of the
                 problem of electronic information overload. This has
                 encouraged interest in developing agents/softbots that
                 can act as electronic personal assistants and can
                 develop and adapt representations of users information
                 needs, commonly known as profiles.

                 As the result of collaborative research with Friends of
                 the Earth, an environmental issues campaigning
                 organisation, we have developed a general purpose
                 information classification agent architecture and have
                 applied it to the problem of document classification
                 and routing. Collaboration with Friends of the Earth
                 allows us to test our ideas in a non-academic context
                 involving high volumes of documents.

                 We use the technique of genetic programming (GP), (Koza
                 and Rice 1992), to evolve classifying agents. This is a
                 novel approach for document classification, where each
                 agent evolves a parse-tree representation of a user's
                 particular information need. The other unusual features
                 of our research are the longevity of our agents and the
                 fact that they undergo a continual training process;
                 feedback from the user enables the agent to adapt to
                 the user's long-term information requirements.",
  notes =        "http://www.isi.edu/isd/AA97/info.html see also
                 clack:1996:adcb",
}