Chunking with Max-Margin Markov Networks
Tang Buzhou, Xuan Wang, Xiaolong Wang · 2008
Abstract. In this paper, we apply Max-Margin Markov Networks (M3Ns) to English base phrases chunking, which is a large margin approach combining both the advantages of graphical models(such as Conditional Random Fields, CRFs) and kernel-based approaches (such as Support Vector Machines, SVMs) to solve the problems of multi-label multi-class supervised classification. To show the efficiency of M3Ns, we compare it with CRFs and other relative systems on the data set of CoNLL-2000 comprehensively. The experiment results show that M3Ns achieves state-of-the-art performance with strong generalization ability, which is better than CRFs.