Inference with Classifiers : The Phrase Identification Problem Inference with Classifiers : The Phrase Identification Problem ∗
Machine learning applications often involve learning several different classifiers and combining their outcomes to a global decision in a way that provides a coherent inference that satisfies some constraints. This paper studies three general approaches to this problem concentrating on identifying sequential structure in the text. In all cases, the classifiers’ learning stage is decoupled from the inference stage. The first two models studied are Markovian approaches. One is a generative model that extends standard HMMs and the second is a conditional model; both allow the use of a rich observation structure and of general classifiers to model state-observation dependencies. The last model studied is an extension of constraint satisfaction formalisms. We develop efficient combination algorithms under all models and study them experimentally in the context of identifying the phrase structure of natural language sentences.