Evolution of Logic Programs: Part-of-Speech Tagging

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{reiser:1999:ELPPT,
  author =       "Philip G. K. Reiser and Patricia J. Riddle",
  title =        "Evolution of Logic Programs: Part-of-Speech Tagging",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and 
                 Marc Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "2",
  pages =        "1338--1346",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 algorithms, inductive logic programming, natural
                 language processing, data mining, ILP, ILP learners,
                 Progol, comprehensible output, concept classification
                 rules, evolutionary algorithm, evolutionary computing,
                 explicit background, greedy ILP algorithm, inductive
                 logic programming, intermediate stages, logic program,
                 logic program evolution, natural language processing
                 problem, part-of-speech tagging, evolutionary
                 computation, inductive logic programming, linguistics,
                 natural languages",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  URL =          "http://www.stancomb.co.uk/~prr/Papers/cec99.pdf",
  URL =          "http://www.stancomb.co.uk/~prr/Papers/cec99.ps",
  DOI =          "doi:10.1109/CEC.1999.782604",
  abstract =     "An algorithm is presented for learning concept
                 classification rules. It is a hybrid between
                 evolutionary computing and inductive logic programming
                 (ILP). Given input of positive and negative examples,
                 the algorithm constructs a logic program to classify
                 these examples. The algorithm has several attractive
                 features, including the ability to use explicit
                 background (user-supplied) knowledge and to produce
                 comprehensible output. We present results of using the
                 algorithm to a natural language processing problem,
                 part-of-speech tagging. The results indicate that using
                 an evolutionary algorithm to direct a population of ILP
                 learners can increase accuracy. This result is further
                 improved when crossover is used to exchange rules at
                 intermediate stages in learning. The improvement over
                 Progol, a greedy ILP algorithm, is statistically
                 significant (P<0.005)",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

Genetic Programming entries for Philip G K Reiser Patricia Jean Riddle

Citations