How can I easily draw a parse tree from Stanford parsing data in python?

Question

So I have this Stanford-style parsing of an english sentence:

"There is a tree behind a car"
Parse: [S [NP There_EX NP] [VP is_VBZ [NP [NP a_DT tree_NN NP] [PP behind_IN [NP a_DT car_NN NP] PP] NP] VP] S]

I want to use some of the tree drawing methods in python to draw a parsing tree from the data.

Is there an easy way to use that parsing representation to draw a tree with python or should I change the representation somehow?

Answer 1

NLTK has a tree module. You can use it to parse the representation you get out of Stanford (see this related question). Then you can use nltk.tree.draw to display it.

Answer 2

Convert the parse into a representation that is understandable by graphviz. Then pass that representation to graphviz. There's also an interefacing library called pygraphviz.

Answer 3

To draw the parsing representation of the tree (and export its resulting visualization), you can alternatively use the Constituent Treelib library (pip install constituent-treelib).

Here are the necessary steps:

from constituent_treelib import ConstituentTree

# First, define the sentence that should be parsed
sentence = "There is a tree behind a car"

# Define the considered language with respect to the underlying spaCy and benepar models 
language = ConstituentTree.Language.English

# Specify the desired model for this language ("Small" is selected by default)
spacy_model_size = ConstituentTree.SpacyModelSize.Medium

# Create the NLP pipeline required to instantiate a ConstituentTree object
nlp = ConstituentTree.create_pipeline(language, spacy_model_size) 

# If you wish, instruct the library to download and install the models automatically
# nlp = ConstituentTree.create_pipeline(language, spacy_model_size, download_models=True) 

# Instantiate a ConstituentTree object and pass it the parsed sentence as well as the NLP pipeline
tree = ConstituentTree(sentence, nlp)

# Finally, draw the tree and export its visualization to a PDF file  
tree.export_tree("my_tree.pdf")

And here is the result...