ML Anthology

The Use of Classifiers in Sequential Inference

Vasin Punyakanok, Dan Roth
NeurIPS 2000 pp. 995-1001
/neurips/2000/punyakanok2000neurips-use/

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 im(cid:173) portant subproblem - identifying phrase structure. The first is a Marko(cid:173) vian approach that extends standard HMMs to allow the use of a rich ob(cid:173) servation structure and of general classifiers to model state-observation dependencies. The second is an extension of constraint satisfaction for(cid:173) malisms. We develop efficient combination algorithms under both mod(cid:173) els and study them experimentally in the context of shallow parsing.

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Cite

Text

Punyakanok and Roth. "The Use of Classifiers in Sequential Inference." Neural Information Processing Systems, 2000.

Markdown

[Punyakanok and Roth. "The Use of Classifiers in Sequential Inference." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/punyakanok2000neurips-use/)

BibTeX

@inproceedings{punyakanok2000neurips-use,
  title     = {{The Use of Classifiers in Sequential Inference}},
  author    = {Punyakanok, Vasin and Roth, Dan},
  booktitle = {Neural Information Processing Systems},
  year      = {2000},
  pages     = {995-1001},
  url       = {https://mlanthology.org/neurips/2000/punyakanok2000neurips-use/}
}