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A dependency tree visualizer for Stanford CoreNLP

Project description

Convert Stanford CoreNLP's Dependency Tree to Spacy's for Visualization

This module provides a format converter from Stanford CoreNLP's dependency trees to Spacy's such that the visualation can be done using Spacy's visualizer.


I just found that Spacy has an amazing visualizer that we should explore more and this project bridges the gap between the CoreNLP parsing outputs and it.


Install Spacy and stanfordnlp. The versions we have tested are Spacy 2.2.2 and stanfordnlp 0.2.0

pip install spacy stanfordnlp

Download Stanford CoreNLP (tested on 3.9.2) Java library:

Set the env variable for stanfordnlp:

export CORENLP_HOME=/yourhome/stanford-corenlp-full-2018-10-05


pip install corenlp-vdep


import spacy
from spacy import displacy
from stanfordnlp.server import CoreNLPClient

from corenlp_dtree_visualizer.converters import _corenlp_dep_tree_to_spacy_dep_tree

# Input text
text = 'Jim killed John with a joke.'

# Get a dependency tree from a Stanford CoreNLP pipeline
with CoreNLPClient(annotators=['tokenize','ssplit','pos','lemma','ner','parse','depparse'],
        timeout=60000, memory='8G', output_format='json') as client:
    # submit the request to the server
    ann = client.annotate(text)

# Convert dependency tree formats
sent = ann['sentences'][0]
tree = _corenlp_dep_tree_to_spacy_dep_tree(sent['tokens'], sent['enhancedPlusPlusDependencies'])

# Visualize with Spacy
nlp = spacy.load("en_core_web_sm")
displacy.render(tree, style="dep", manual=True)

# could also save to a file
# svg = displacy.render(tree, style="dep", manual=True)
# with open('tmp.svg', 'w', encoding='utf-8') as fw:
    # fw.write(svg)


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