2001/11/01 by Vasin Punyakanok, Dan Roth
Computer Science · #cs.LG #cs.CL
published as Advances in Neural Information Processing Systems 13 · 7 pages, 1 figure
arxiv created 2001/11/01 · arxiv updated 2009/11/30
We study the problem of combining the outcomes of several different classifiers in a way that provides a coherent inference that satisfies some constraints. In particular, we develop two general approaches for an important subproblem-identifying phrase structure. The first is a Markovian approach that extends standard HMMs to allow the use of a rich observation structure and of general classifiers to model state-observation dependencies. The second is an extension of constraint satisfaction formalisms. We develop efficient combination algorithms under both models and study them experimentally in the context of shallow parsing.