Parsing as Reduction
Table 1: Results on English PTB §22 achieved by various d-parsers and encoding strategies. For dependencies, we report unlabeled/labeled attachment scores (UAS/LAS), excluding punctuation. For constituents, we show F1-scores (without punctuation and root nodes), as provided by EVALB [Black et al.1992]. We report total parsing speeds in tokens per second (including time spent on pruning, decoding, and feature evaluation), measured on a Intel Xeon processor @2.30GHz.
['[BOLD] Dependency Parser', '[BOLD] UAS', '[BOLD] LAS', '[BOLD] F1', '[BOLD] #Toks/s.']
For the training portion of the English PTB, which contains 27 non-terminal symbols (excluding the POS tags), the direct encoding strategy yields 75 labels, while delta encoding yields 69 labels (averaging 2.6 indices per symbol). By contrast, the HN-encoding procedure yields 731 labels, more than 10 times as many. In our experiments, we found it advantageous to perform labeled d-parsing in two stages, as done by \newciteMcDonald2006CoNLL: first, train an unlabeled d-parser; then, train a dependency labeler. For TurboParser, we used basic, standard and full models. Since two-stage TP-Full with delta-encoding is the best strategy, we use this configuration in the subsequent experiments.