Flexible Margin Selection for Reranking with Full Pairwise Samples
Perceptron like large margin algorithms are introduced for the experiments with various margin selections. Compared to the previous perceptron reranking algorithms, the new algorithms use full pairwise samples and allow us to search for margins in a larger space. Our experimental results on the data set of  show that a perceptron like ordinal regression algorithm with uneven margins can achieve Recall/Precision of 89.5/90.0 on section 23 of Penn Treebank. Our result on margin selection can be employed in other large margin machine learning algorithms as well as in other NLP tasks.