The usual procedure of optimizing hidden Markov Models for data likelihood has undesirable consequences in information extraction: it focuses attention on the data rather than on the labeling task. Often, joint likelihood is poorly correlated with extraction F1. We demonstrate that optimizing the conditional likelihood of the target labels addresses these limitations and is more indicative of t...