@inproceedings{91616fb5dd474bdaac2cfdc16fef803c,
title = "The use of classifiers in sequential inference",
abstract = "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.",
author = "Vasin Punyakanok and Dan Roth",
year = "2001",
language = "English (US)",
isbn = "0262122413",
series = "Advances in Neural Information Processing Systems",
publisher = "Neural information processing systems foundation",
booktitle = "Advances in Neural Information Processing Systems 13 - Proceedings of the 2000 Conference, NIPS 2000",
note = "14th Annual Neural Information Processing Systems Conference, NIPS 2000 ; Conference date: 27-11-2000 Through 02-12-2000",
}