%0 Conference Paper %1 reiser:1999:ELPPT %A Reiser, Philip G. K. %A Riddle, Patricia J. %B Proceedings of the Congress on Evolutionary Computation %C Mayflower Hotel, Washington D.C., USA %D 1999 %E Angeline, Peter J. %E Michalewicz, Zbyszek %E Schoenauer, Marc %E Yao, Xin %E Zalzala, Ali %I IEEE Press %K ILP algorithms, data evolutionary genetic inductive language logic mining, natural processing, programming, %P 1338--1346 %T Evolution of Logic Programs: Part-of-Speech Tagging %U http://www.stancomb.co.uk/~prr/Papers/cec99.ps %V 2 %X 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) %@ 0-7803-5537-7 (Microfiche)

@inproceedings{reiser:1999:ELPPT, 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)}, added-at = {2008-06-19T17:46:40.000+0200}, address = {Mayflower Hotel, Washington D.C., USA}, author = {Reiser, Philip G. K. and Riddle, Patricia J.}, biburl = {https://www.bibsonomy.org/bibtex/2555a009db482ff7269d484df80a8a220/brazovayeye}, booktitle = {Proceedings of the Congress on Evolutionary Computation}, editor = {Angeline, Peter J. and Michalewicz, Zbyszek and Schoenauer, Marc and Yao, Xin and Zalzala, Ali}, interhash = {4eeb112c2d13843b0c3e95f55fcb19e7}, intrahash = {555a009db482ff7269d484df80a8a220}, isbn = {0-7803-5537-7 (Microfiche)}, keywords = {ILP algorithms, data evolutionary genetic inductive language logic mining, natural processing, programming,}, month = {6-9 July}, notes = {CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143}, organisation = {Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE}, pages = {1338--1346}, publisher = {IEEE Press}, publisher_address = {445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA}, timestamp = {2008-06-19T17:50:11.000+0200}, title = {Evolution of Logic Programs: Part-of-Speech Tagging}, url = {http://www.stancomb.co.uk/~prr/Papers/cec99.ps}, volume = 2, year = 1999 }