Introduction: Most research in machine learning has been focused on binary classification, in which the learned classifier outputs one of two possible answers. Important fundamental questions can be analyzed in terms of binary classification, but realworld natural language processing problems often involve richer output spaces. In this tutorial, we will focus on classifiers with a large number of possible outputs with interesting structure. Notable examples include information retrieval, part-of-speech tagging, NP chucking, parsing, entity extraction, and phoneme recognition. .
