  Conditional random fields (CRFs) are usually specified by graphical models
but in this paper we propose to use probabilistic logic programs and specify
them generatively. Our intension is first to provide a unified approach to CRFs
for complex modeling through the use of a Turing complete language and second
to offer a convenient way of realizing generative-discriminative pairs in
machine learning to compare generative and discriminative models and choose the
best model. We implemented our approach as the D-PRISM language by modifying
PRISM, a logic-based probabilistic modeling language for generative modeling,
while exploiting its dynamic programming mechanism for efficient probability
computation. We tested D-PRISM with logistic regression, a linear-chain CRF and
a CRF-CFG and empirically confirmed their excellent discriminative performance
compared to their generative counterparts, i.e.\ naive Bayes, an HMM and a
PCFG. We also introduced new CRF models, CRF-BNCs and CRF-LCGs. They are CRF
versions of Bayesian network classifiers and probabilistic left-corner grammars
respectively and easily implementable in D-PRISM. We empirically showed that
they outperform their generative counterparts as expected.
