The Use of Classifiers in Sequential Inference
<|reference_start|>The Use of Classifiers in Sequential Inference: 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.<|reference_end|>
@article{punyakanok2001the,
    title={The Use of Classifiers in Sequential Inference},
    author={Vasin Punyakanok, Dan Roth},
    journal={Advances in Neural Information Processing Systems 13},
    year={2001},
    archivePrefix={arXiv},
    eprint={cs/0111003},
    primaryClass={cs.LG cs.CL}
}