Sequential Learning of Classifiers for Structured Prediction Problems


Dan Roth, Kevin Small, Ivan Titov ;
Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:440-447, 2009.


Many classification problems with structured outputs can be regarded as a set of interrelated sub-problems where constraints dictate valid variable assignments. The standard approaches to these problems include either independent learning of individual classifiers for each of the sub-problems or joint learning of the entire set of classifiers with the constraints enforced during learning. We propose an intermediate approach where we learn these classifiers in a sequence using previously learned classifiers to guide learning of the next classifier by enforcing constraints between their outputs. We provide a theoretical motivation to explain why this learning protocol is expected to outperform both alternatives when individual problems have different ‘complexity’. This analysis motivates an algorithm for choosing a preferred order of classifier learning. We evaluate our technique on artificial experiments and on the entity and relation identification problem where the proposed method outperforms both joint and independent learning.

Related Material