In case someone wants to easily view the dependency tree produced by spacy, one solution would be to convert it to an nltk.tree.Tree
and use the nltk.tree.Tree.pretty_print
method. Here is an example:
import spacy
from nltk import Tree
en_nlp = spacy.load('en')
doc = en_nlp("The quick brown fox jumps over the lazy dog.")
def to_nltk_tree(node):
if node.n_lefts + node.n_rights > 0:
return Tree(node.orth_, [to_nltk_tree(child) for child in node.children])
else:
return node.orth_
[to_nltk_tree(sent.root).pretty_print() for sent in doc.sents]
Output:
jumps
________________|____________
| | | | | over
| | | | | |
| | | | | dog
| | | | | ___|____
The quick brown fox . the lazy
Edit: For changing the token representation you can do this:
def tok_format(tok):
return "_".join([tok.orth_, tok.tag_])
def to_nltk_tree(node):
if node.n_lefts + node.n_rights > 0:
return Tree(tok_format(node), [to_nltk_tree(child) for child in node.children])
else:
return tok_format(node)
Which results in:
jumps_VBZ
__________________________|___________________
| | | | | over_IN
| | | | | |
| | | | | dog_NN
| | | | | _______|_______
The_DT quick_JJ brown_JJ fox_NN ._. the_DT lazy_JJ
与恶龙缠斗过久,自身亦成为恶龙;凝视深渊过久,深渊将回以凝视…