Stacking Heterogeneous Joint Models of Chinese POS Tagging and Dependency Parsing

Previous joint models of Chinese part-of-speech (POS) tagging and dependency parsing are extended from either graph- or transition-based dependency models. Our analysis shows that the two models have different error distributions. In addition, integration of graph- and transition-based dependency parsers by stacked learning (stacking) has achieved significant improvements. These motivate us to study the problem of stacking graph- and transition-based joint models. We conduct experiments on Chinese Penn Treebank 5.1 (CTB5.1). The results demonstrate that the guided transition-based joint model obtains better performance than the guided graph-based joint model. Further, we introduce a constituent-based joint model which derives the POS tag sequence and dependency tree from the output of PCFG parsers, and then integrate it into the guided transition-based joint model. Finally, we achieve the best performance on CTB5.1, 94.95 % in tagging accuracy and 83.98 % in parsing accuracy respectively