An Empirical Comparison of Parsing Methods for Stanford Dependencies
Table 1: Basic SD parsing performance and runtime. ∗EasyFirst and Huang do not include dependency labeling in the runtime. †When training the Huang parser, we provide §23 (§22) as development data when testing on §22 (§23), which gives a slight advantage. ‡Charniak-Johnson uses semi-supervised training; Stanford englishPCFG and RNN models use external resources; see text.
['[EMPTY]', 'Type', 'Parser', 'PTB §22 UAS', 'PTB §22 LAS', 'PTB §23 UAS', 'PTB §23 LAS', 'Speed (tokens/s.)']
The most accurate approach is still to use the Charniak-Johnson parser (4), though Full TurboParser (10) is the best among d-parsing techniques, lagging Charniak-Johnson by 2–3 absolute points and with about twice the speed. If the Stanford englishPCFG model provides adequate accuracy for a downstream application, then we advise using MSTParser or any variant of TurboParser instead. In particular, without sacrificing the Stanford englishPCFG’s level of performance, Basic TurboParser runs nearly 20 times faster.