I read the paper --
S. Patwardhan and E Riloff. 2009. A uniﬁed model of phrasal and sentential evidence for information extraction. In EmpiricalMethods in Natural Language Processing (EMNLP).
This paper combines two components to jointly model the probability using the following factorized distribution:
P(EvSent(SNPi),PlausFillr(NPi )|F) =
P(EvSent(SNPi)|F) ∗ P(PlausFillr(NPi )|EvSent(SNPi), F)
The first component of models is the "sentential event recognizer" which uses sentence level features and the second component is the "plausible role filler recognizer." For the sentence classifier, they tried Naive Bayes but (as is expected) it does not give very good probability estimates. Therefore, they decided to use an SVM and normalize the scores to get a probability. They also used NB for the role filler recognizer.
From an ML/statistical perspective this is far simpler model than the focus paper which uses a more complicated graphical model that and jointly trains all the components of the model, even using unannotated data.