Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data. Sutton, C. A., Rohanimanesh, K., & McCallum, A. In Machine Learning, Proceedings of the Twenty-first International Conference (ICML), Banff, Alberta, Canada, July 4-8, 2004, volume 69, of ACM International Conference Proceeding Series, 2004. ACM. ![Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi bibtex @inproceedings{DBLP:conf/icml/SuttonRM04,
author = {Charles A. Sutton and Khashayar Rohanimanesh and Andrew McCallum},
bibsource = {dblp computer science bibliography, http://dblp.org},
biburl = {http://dblp.org/rec/bib/conf/icml/SuttonRM04},
booktitle = {Machine Learning, Proceedings of the Twenty-first International Conference ({ICML}), Banff, Alberta, Canada, July 4-8, 2004},
doi = {10.1145/1015330.1015422},
editor = {Carla E. Brodley},
url = {http://doi.acm.org/10.1145/1015330.1015422},
publisher = {ACM},
series = {{ACM} International Conference Proceeding Series},
timestamp = {Mon, 22 Oct 2007 13:54:01 +0200},
title = {Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data},
volume = {69},
year = {2004},
sum = {Joint inference over two traditionally-separate layers of NLP processing: POS-tagging and NP-chunking. Introduces the CRF analogue of Factorial HMMs. Compares several approximate inference procedures.}
}
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