Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data Export

@inproceedings{citeulike:330532, abstract = {We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions made in those models. Conditional random fields also avoid a fundamental limitation of maximum entropy Markov models (MEMMs) and other discriminative Markov models based on directed graphical models, ...}, author = {Lafferty, John and Mccallum, Andrew and Pereira, Fernando}, booktitle = {Proc. 18th International Conf. on Machine Learning}, citeulike-article-id = {330532}, citeulike-linkout-0 = {http://citeseer.ist.psu.edu/lafferty01conditional.html}, citeulike-linkout-1 = {http://citeseer.lcs.mit.edu/lafferty01conditional.html}, citeulike-linkout-2 = {http://citeseer.ifi.unizh.ch/lafferty01conditional.html}, citeulike-linkout-3 = {http://citeseer.comp.nus.edu.sg/lafferty01conditional.html}, keywords = {conditional, crf, labeling, log-linear, models, sequence}, pages = {282--289}, posted-at = {2009-09-30 14:28:43}, priority = {2}, publisher = {Morgan Kaufmann, San Francisco, CA}, title = {Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data}, url = {http://citeseer.ist.psu.edu/lafferty01conditional.html}, year = {2001} }

TY - CONF ID - citeulike:330532 L3 - citeulike-article-id:330532 N2 - We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions made in those models. Conditional random fields also avoid a fundamental limitation of maximum entropy Markov models (MEMMs) and other discriminative Markov models based on directed graphical models, ... EP - 289 TI - Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data PB - Morgan Kaufmann, San Francisco, CA T2 - Proc. 18th International Conf. on Machine Learning SP - 282 KW - conditional KW - crf KW - labeling KW - log-linear KW - models KW - sequence AU - Lafferty, John AU - Mccallum, Andrew AU - Pereira, Fernando PY - 2001/// UR - http://citeseer.ist.psu.edu/lafferty01conditional.html ER -