Fast discovery of association rules
Advances in knowledge discovery and data mining
Advances in Inductive Logic Programming
Advances in Inductive Logic Programming
Redundant Covering with Global Evaluation in the RC1 Inductive Learner
SBIA '98 Proceedings of the 14th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Part-of-Speech Tagging Using Progol
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Induction of first-order decision lists: results on learning the past tense of English verbs
Journal of Artificial Intelligence Research
Integrating Rules and Cases in Learning via Case Explanation and Paradigm Shift
IBERAMIA-SBIA '00 Proceedings of the International Joint Conference, 7th Ibero-American Conference on AI: Advances in Artificial Intelligence
Combining Rule-Based and Case-Based Learning for Iterative Part-of-Speech Tagging
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
An experiment with association rules and classification: post-bagging and conviction
DS'05 Proceedings of the 8th international conference on Discovery Science
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Assigning a category to a given word (tagging) depends on the particular word and on the categories (tags) of neighboring words. A theory that is able to assign tags to a given text can naturally be viewed as a recursive logic program. This article describes how iterative induction, a technique that has been proven powerful in the synthesis of recursive logic programs, has been applied to the task of part-of-speech tagging. The main strategy consists of inducing a succession T1, T2,..., Tn of theories, using in the induction of theory Ti all the previously induced theories. Each theory in the sequence may have lexical rules, context rules and hybrid ones. This iterative strategy is, to a large extent, independent of the inductive algorithm underneath. Here we consider one particular relational learning algorithm, CSC(RC), and we induce first order theories from positive examples and background knowledge that are able to successfully tag a relatively large corpus in Portuguese.