Evidence that this is the most accurate parser is here;
the previous approach mentioned is a March 2016 paper, &quot;Globally Normalized Transition-Based Neural Networks,&quot;  <a href="http:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;1603.06042" rel="nofollow">http:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;1603.06042</a><p>&quot;On a standard benchmark consisting of randomly drawn English newswire sentences (the 20 year old Penn Treebank), Parsey McParseface recovers individual dependencies between words with over 94% accuracy, beating our own previous state-of-the-art results, which were already better than any previous approach.&quot;<p>From the original paper, &quot;Our model achieves state-of-the-art accuracy
on all of these tasks, matching or outperforming
LSTMs while being significantly faster.
In particular for dependency parsing on the Wall
Street Journal we achieve the best-ever published
unlabeled attachment score of 94.41%.&quot;<p>This seems like a narrower standard than described, specifically being better at parsing the Penn Treebank than the best natural language parser for English on the Wall Street Journal.<p>The statistics listed on the project GitHub actually contradict these claims by showing the original March 2016 implementation has higher accuracy than Parsey McParseface.