Evolution of Logic Programs: Part-of-Speech Tagging

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@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