redshift

Accuracy is state-of-the-art -- 92-93% depending on the beam width and the evaluation set (Stanford or MALT dependencies). I assume that this is for English? A former colleague of mine compared two statistical dependency parsers (Malt and MST) to a rule-based dependency parser with a maxent disambiguation model, for Dutch. The rule-based system outperforms the statistical dependency parsers by a wide margin, both in-domain and out-of-domain: http://dl.acm.org/citation.cfm?id=1870171 Nonetheless, I find work on statistical dependency parsing to be very exiting, since it is fast and requires far less human effort :). https://github.com/syllog1sm/redshift/ . You'll want the develop branch. It's GPL licensed. Very nice work!

The Stanford parser is particularly slow --- it's in java, and it's written for research more than anything. The C&C CCG parser runs at about 60-80 sentences a second, although it gives either CCG constituents or dependencies -- so the output may take some interpretation. Shift-reduce dependency parsers are linear time, and are giving state-of-the-art results. My parser's currently a pain in the ass to install, as it hasn't really been released yet, but it does hundreds of sentences a second. Accuracy is state-of-the-art -- 92-93% depending on the beam width and the evaluation set (Stanford or MALT dependencies). https://github.com/syllog1sm/redshift/ . You'll want the develop branch. It's GPL licensed. It's implemented in Cython (i.e., almost all the code is Cython --- I'm not using it just for the speed critical bits), which would make