Previous abstract | Contents | Next abstract

Shallow Parsing by Inferencing with Classifiers

We study the problem of identifying phrase structure. We formalize it as the problem of combining the outcomes of several different classifiers in a way that provides a coherent inference that satisfies some constraints, and develop two general approaches for it. The first is a Markovian approach that extends standard HMMs to allow the use of a rich observations structure and of general classifiers to model state-observation dependencies. The second is an extension of constraint satisfaction formalisms. We also develop efficient algorithms under both models and study them experimentally in the context of shallow parsing.