This paper describes the implementation of a system that automatically learns selectional restrictions for individual senses of polysemous verbs from subject-object relationships....
In this article we compare the performance of various machine learning algorithms on the task of constructing word-sense disambiguation rules from data. The distinguishing characte...
Georgios Paliouras, Vangelis Karkaletsis, Ion Andr...
The aim of this paper is to show how abduction can be used in classification tasks when we deal with incomplete data. Some classifiers, even if based on decision tree induction lik...
Abstract. Partial parsing techniques try to recover syntactic information efficiently and reliably by sacrificing completeness and depth of analysis. One of the difficulties of pa...
Decision trees are widely disseminated as an effective solution for classification tasks. Decision tree induction algorithms have some limitations though, due to the typical strat...
The label ranking problem consists of learning a model that maps instances to total orders over a finite set of predefined labels. This paper introduces new methods for label ra...
Decision tree induction techniques attempt to find small trees that fit a training set of data. This preference for smaller trees, which provides a learning bias, is often justifie...
Christian Bessiere, Emmanuel Hebrard, Barry O'Sull...
This paper extends previous work on skewing, an approach to problematic functions in decision tree induction. The previous algorithms were applicable only to functions of binary v...