The Use of Classifiers in Sequential Inference
| dc.creator | Punyakanok, Vasin | |
| dc.creator | Roth, Dan | |
| dc.date | 2001-11-01 | |
| dc.date.accessioned | 2026-07-07T03:17:52Z | |
| dc.date.available | 2026-07-07T03:17:52Z | |
| dc.description | 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. | |
| dc.description | 7 pages, 1 figure | |
| dc.identifier | https://arxiv.org/abs/cs/0111003 | |
| dc.identifier | http://arxiv.org/abs/cs/0111003 | |
| dc.identifier | Advances in Neural Information Processing Systems 13 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/30888 | |
| dc.subject | Machine Learning | |
| dc.subject | Computation and Language | |
| dc.subject | I.2.6, I.2.7 | |
| dc.title | The Use of Classifiers in Sequential Inference | |
| dc.type | text |