173b155 Nov 6, 2017
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#!/usr/bin/env python
# coding: utf8
"""This example shows how to navigate the parse tree including subtrees
attached to a word.
Based on issue #252:
"In the documents and tutorials the main thing I haven't found is
examples on how to break sentences down into small sub thoughts/chunks. The
noun_chunks is handy, but having examples on using the token.head to find small
(near-complete) sentence chunks would be neat. Lets take the example sentence:
"displaCy uses CSS and JavaScript to show you how computers understand language"
This sentence has two main parts (XCOMP & CCOMP) according to the breakdown:
[displaCy] uses CSS and Javascript [to + show]
show you how computers understand [language]
I'm assuming that we can use the token.head to build these groups."
Compatible with: spaCy v2.0.0+
from __future__ import unicode_literals, print_function
import plac
import spacy
model=("Model to load", "positional", None, str))
def main(model='en_core_web_sm'):
nlp = spacy.load(model)
print("Loaded model '%s'" % model)
doc = nlp("displaCy uses CSS and JavaScript to show you how computers "
"understand language")
# The easiest way is to find the head of the subtree you want, and then use
# the `.subtree`, `.children`, `.lefts` and `.rights` iterators. `.subtree`
# is the one that does what you're asking for most directly:
for word in doc:
if word.dep_ in ('xcomp', 'ccomp'):
print(''.join(w.text_with_ws for w in word.subtree))
# It'd probably be better for `word.subtree` to return a `Span` object
# instead of a generator over the tokens. If you want the `Span` you can
# get it via the `.right_edge` and `.left_edge` properties. The `Span`
# object is nice because you can easily get a vector, merge it, etc.
for word in doc:
if word.dep_ in ('xcomp', 'ccomp'):
subtree_span = doc[word.left_edge.i : word.right_edge.i + 1]
print(subtree_span.text, '|', subtree_span.root.text)
# You might also want to select a head, and then select a start and end
# position by walking along its children. You could then take the
# `.left_edge` and `.right_edge` of those tokens, and use it to calculate
# a span.
if __name__ == '__main__':
# Expected output:
# to show you how computers understand language
# how computers understand language
# to show you how computers understand language | show
# how computers understand language | understand