Combining Rule-Based and Case-Based Learning for Iterative Part-of-Speech Tagging

In this article we show how the accuracy of a rule based first order theory may be increased by combining it with a case-based approach in a classification task. Case-based learning is used when the rule language bias is exhausted. This is achieved in an iterative approach. In each iteration theories consisting of first order rules are induced and covered examples are removed. The process stops when it is no longer possible to find rules with satisfactory quality. The remaining examples are then handled as cases. The case-based approach proposed here is also, to a large extent, new. Instead of only storing the cases as provided, it has a learning phase where, for each case, it constructs and stores a set of explanations with support and confidence above given thresholds. These explanations have different levels of generality and the maximally specific one corresponds to the case itself. The same case may have different explanations representing different perspectives of the case. Therefore, to classify a new case, it looks for relevant stored explanations applicable to the new case. The different possible views of the case given by the explanations correspond to considering different sets of conditions/features to analyze the case. In other words, they lead to different ways to compute similarity between known cases/explanations and the new case to be classified (as opposed to the commonly used global metric). Experimental results have been obtained on a corpus of Portuguese texts for the task of part-of-speech tagging with significant improvement.