Comparison of Various Approaches for Dependency Parsing

This paper presents the comparison results of dependency parsing for two distinct languages: Kazakh and English, by using a various discrete and distributed feature-based approaches We apply graph/transition-based methods to train these models and to report the typed and untyped accuracy. Different comparisons are made for comparing these models by utilizing discrete or dense features. Experimental results show that discrete feature-based approaches (graph-based) perform well than others when the size of data-set is relatively small. For a large data set, the results of those approaches are very competitive with each other, and no significant difference in performance can be observed. In terms of training speed, the results show that discrete feature-based parsers take much less training time than the neural network-based parser, but with comparable performances.