I have a corpus of a few 100-thousand legal documents (mostly from the European Union) – laws, commentary, court documents etc. I am trying to algorithmically make some sense of them. I have model...

I've installed syntaxnet and am able to run the parser with the provided demo script. Ideally, I would like to run it directly from python. The only code I found was this: import subprocess import...

Parsey McParsey returns a dependency-based parse tree by default, but is their a way to get a constituency-based parse tree from it? EDIT: To clarify, by "to get from it" I mean from the Parsey it...

I have the demo.sh working fine and I've looked at the parser_eval.py and grokked it all to some extent. However, I don't see how to serve this model using TensorFlow Serving. There are two issues ...

The POS tags and Depedency labels output by Parsey McParseface are given in the tag-set and label-set files here respectively. The Syntaxnet readme outlines that the model was trained on the Penn ...