mirror of https://github.com/explosion/spaCy.git
60 lines
2.4 KiB
Python
60 lines
2.4 KiB
Python
"""Issue #252
|
|
|
|
Question:
|
|
|
|
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 on https://displacy.spacy.io/displacy/index.html
|
|
|
|
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. In one of your examples you had the following function.
|
|
|
|
def dependency_labels_to_root(token):
|
|
'''Walk up the syntactic tree, collecting the arc labels.'''
|
|
dep_labels = []
|
|
while token.head is not token:
|
|
dep_labels.append(token.dep)
|
|
token = token.head
|
|
return dep_labels
|
|
"""
|
|
from __future__ import print_function, unicode_literals
|
|
|
|
# Answer:
|
|
# 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:
|
|
|
|
from spacy.en import English
|
|
nlp = English()
|
|
|
|
doc = nlp(u'displaCy uses CSS and JavaScript to show you how computers understand language')
|
|
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.
|
|
|
|
doc = nlp(u'displaCy uses CSS and JavaScript to show you how computers understand language')
|
|
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)
|
|
print(subtree_span.similarity(doc))
|
|
print(subtree_span.similarity(subtree_span.root))
|
|
|
|
|
|
# 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.
|
|
|
|
|
|
|