Its main characteristics are the possibility to include a huge number of features, like the Maximum Entropy (ME) model, but computing global conditional probabilities normalized at sentence level, instead of position level like in ME.
this last point results very effective since it solves the label bias problem, as pointed out in (Lafferty et al., 2001).