A Survey of Syntactic-Semantic Parsing Based on Constituent and Dependency Structures
Table 2: A comparison of representative dependency parsing models, where UAS are reported, PTB and CTB5.1 (CTB in the Table for short) are two benchmark datasets for the English and Chinese parsing, respectively.
['Model', 'Main Features', 'PTB', 'CTB']
The majority of dependency parsing models can be divided into two types, graph-based and transition-based [nivre-mcdonald-2008-integrating], both of which have been extensively investigated under the traditional statistical setting [mcdonald-etal-2005-online, XavierCarreras2007, nivre-iwpt03, Yamada2003] and the neural setting [nivre-mcdonald-2008-integrating]. There also exist other interesting approaches for dependency parsing outside the two categories [li-etal-2018-seq2seq]. The graph-based and transition-based models are almost comparable (graph-based models are slightly higher) across both traditional statistical and neural settings, and other types of models achieve good performance with the support of sophisticated neural networks. Currently， neural models achieve state-of-the-art performances for dependency parsing [kulmizev-etal-2019-deep].