A Selective Sampling Strategy for Label Ranking [chapter]

Massih Amini, Nicolas Usunier, François Laviolette, Alexandre Lacasse, Patrick Gallinari
2006 Lecture Notes in Computer Science  
We propose a novel active learning strategy based on the compression framework of [9] for label ranking functions which, given an input instance, predict a total order over a predefined set of alternatives. Our approach is theoretically motivated by an extension to ranking and active learning of Kääriäinen's generalization bounds using unlabeled data [7] , initially developed in the context of classification. The bounds we obtain suggest a selective sampling strategy provided that a
more » ... , yet reasonably large initial labeled dataset is provided. Experiments on Information Retrieval corpora from automatic text summarization and question/answering show that the proposed approach allows to substantially reduce the labeling effort in comparison to random and heuristic-based sampling strategies.
doi:10.1007/11871842_7 fatcat:y4eiq5yuxrbd5i7xomxgcl5tmm