Those are the part-of-speech tag accuracies. spaCy&#x27;s accuracy on the PTB evaluation is 92.2% --- so it makes 20% more errors than P. McP. On the other hand, spaCy is about 200x faster.<p>I&#x27;ve been watching the line of research in SyntaxNet closely, and have been steadily working on replacing spaCy&#x27;s averaged perceptron model with a neural network model. This is one of the main differences between spaCy and Parser McParseface.<p>The key advantage of the neural network is that it lets you take advantage of training on lots and lots more text, in a semi-supervised way. In a linear model, you grow extra parameters when you do this. The neural network stays the same size --- it just gets better. So, you can benefit from reading the whole web into the neural network. This only works a little bit in the linear model, and it makes the resulting model enormous.<p>Another difference is that spaCy is trained on whole documents, while P. McP. is trained in the standard set-up, using gold pre-processing. I speculate this will reduce the gap between the systems in a more realistic evlauation. Of course, P. McP can do the joint training too if they choose to. I&#x27;ve reached out to see whether they&#x27;re interested in running the experiment: <a href="https:&#x2F;&#x2F;github.com&#x2F;tensorflow&#x2F;models&#x2F;issues&#x2F;65" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;tensorflow&#x2F;models&#x2F;issues&#x2F;65</a>