An Empirical Comparison of Parsing Methods for Stanford Dependencies
Table 6: Integrating Yamada-Matsumoto dependencies into a Basic SD parser. Numerals in the leftmost column refer to rows in Table 1. “MaltParser” refers to the liblinear stackproj version.
['[EMPTY]', 'Parser', 'PTB §22 UAS', 'PTB §22 LAS', 'PTB §23 UAS', 'PTB §23 LAS', 'Speed (tokens/s.)']
First, we find that all four combinations outperform MSTParser on its own. The gains are usually smallest when the same parser (MSTParser) and representation (SD) are used at both levels. Changing either the first parser’s representation (to YM) or algorithm (to MaltParser) gives higher performance, but varying the representation is more important, with YM features giving a 1.5% absolute gain on LAS over MSTParser. The runtime is roughly doubled; this is what we would expect, since stacking involves running two parsers in sequence.