Second, we used a large-margin perceptron, similar to Surdeanu, Ciaramita, and Zaragoza (2011).
we update the model not only when the predicted answer is incorrect (line 5), but also when the current model is not confident enough-that is, when the predicted answer is correct, but the difference in F scores between this answer and the second predicted answer is smaller than a small positive hyper parameter τ.