Comment by elliptic

Can someone explain the following comments, for someone with some knowledge of ML but none of NLP? "First, it's really much better to use Averaged Perceptron, or some other method which can be trained in an error-driven way. You don't want to do batch learning. Batch learning makes it difficult to train from negative examples effectively, and this makes a very big difference to accuracy" I thought that it was typical for suitably regularized batch methods to modestly outperform or at least match (in terms of accuracy) online methods, whose main advantage is their speed.