Paper: A Sequential Model For Multi-Class Classification

ACL ID W01-0502
Title A Sequential Model For Multi-Class Classification
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2001

Many classification problems require decisions among a large number of competing classes. These tasks, however, are not handled well by general pur- pose learning methods and are usually addressed in an ad-hoc fashion. We suggest a general approach – a sequential learning model that utilizes classi- fiers to sequentially restrict the number of compet- ing classes while maintaining, with high probability, the presence of the true outcome in the candidates set. Some theoretical and computational properties of the model are discussed and we argue that these are important in NLP-like domains. The advantages of the model are illustrated in an experiment in part- of-speech tagging.