is trained in the standard set-up, using gold pre-processing. This only works a little bit in the linear model, and it makes the resulting model enormous.Īnother difference is that spaCy is trained on whole documents, while P. So, you can benefit from reading the whole web into the neural network. The neural network stays the same size - it just gets better. In a linear model, you grow extra parameters when you do this. 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. This is one of the main differences between spaCy and Parser McParseface. I've been watching the line of research in SyntaxNet closely, and have been steadily working on replacing spaCy's averaged perceptron model with a neural network model. On the other hand, spaCy is about 200x faster. spaCy's accuracy on the PTB evaluation is 92.2% - so it makes 20% more errors than P. Those are the part-of-speech tag accuracies. The statistics listed on the project GitHub actually contradict these claims by showing the original March 2016 implementation has higher accuracy than Parsey McParseface. 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. Street Journal we achieve the best-ever published

In particular for dependency parsing on the Wall On all of these tasks, matching or outperforming '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.'įrom the original paper, 'Our model achieves state-of-the-art accuracy The previous approach mentioned is a March 2016 paper, 'Globally Normalized Transition-Based Neural Networks,' Evidence that this is the most accurate parser is here

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