Investigating Loss Functions and Optimization Methods for Discriminative Learning of Label Sequences (2003) [18 citations — 1 self]
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by
Yasemin Altun
,
Mark Johnson
,
Thomas Hofmann
In Proc. EMNLP
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@INPROCEEDINGS{Altun03investigatingloss,
author = {Yasemin Altun and Mark Johnson and Thomas Hofmann},
title = {Investigating Loss Functions and Optimization Methods for Discriminative Learning of Label Sequences},
booktitle = {In Proc. EMNLP},
year = {2003}
}
author = {Yasemin Altun and Mark Johnson and Thomas Hofmann},
title = {Investigating Loss Functions and Optimization Methods for Discriminative Learning of Label Sequences},
booktitle = {In Proc. EMNLP},
year = {2003}
}
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Abstract:
Discriminative models have been of interest in the NLP community in recent years.
Citations
| 848 | Conditional random fields: Probabilistic models for segmenting and labeling sequence data – Lafferty, McCallum, et al. - 2001 |
| 400 | Improved boosting algorithms using confidence-rated predictions – Schapire, Singer - 1999 |
| 259 | Maximum entropy markov models for information extraction and segmentation – McCallum, Freitag, et al. - 2000 |
| 103 | Learning to parse natural language with maximum entropy models – Ratnaparkhi - 1999 |
| 97 | Estimators for stochastic ―Unification-based‖ grammars – Johnson, Geman, et al. - 1999 |
| 69 | D.: The use of classifiers in sequential inference – Punyakanok, Roth |
| 31 | Conditional structure versus conditional estimation in NLP models – Klein, Manning - 2002 |
| 20 | Discriminative learning for label sequences via boosting – Altun, Hofmann, et al. - 2002 |
| 17 | An alternate objective function for markovian fields – Kakade, Teh, et al. - 2002 |
| 16 | An algorithm that learns what’s in a name – Weischedel - 1999 |
