Greedy Transition-Based Dependency Parsing withDiscrete and Continuous Supertag Features
Table 2: Accuracy of different parsing models on the development set; BL = baseline, BS = best supertag, SD = supertag distribution.
['[BOLD] Features', '[BOLD] UAS', '[BOLD] LAS']
The SD model in this case is trained with k=320 principal components. We see that adding supertag features to the baseline model improves parsing accuracy significantly, regardless of whether we use the BS or the SD model. When used by themselves, the BS model gives a slightly larger improvement than the SD model, but a combination of the two models is better than any of the models by themselves. This indicates that the distributional supertag model captures partly different information from the best supertag model.