Evolution of logic programs: part-of-speech tagging

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).