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60 lines
2.4 KiB
Python
60 lines
2.4 KiB
Python
"""Issue #252
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Question:
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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.
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Lets take the example sentence on https://displacy.spacy.io/displacy/index.html
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displaCy uses CSS and JavaScript to show you how computers understand language
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This sentence has two main parts (XCOMP & CCOMP) according to the breakdown:
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[displaCy] uses CSS and Javascript [to + show]
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&
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show you how computers understand [language]
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I'm assuming that we can use the token.head to build these groups. In one of your examples you had the following function.
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def dependency_labels_to_root(token):
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'''Walk up the syntactic tree, collecting the arc labels.'''
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dep_labels = []
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while token.head is not token:
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dep_labels.append(token.dep)
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token = token.head
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return dep_labels
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"""
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from __future__ import print_function, unicode_literals
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# Answer:
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# The easiest way is to find the head of the subtree you want, and then use the
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# `.subtree`, `.children`, `.lefts` and `.rights` iterators. `.subtree` is the
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# one that does what you're asking for most directly:
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from spacy.en import English
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nlp = English()
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doc = nlp(u'displaCy uses CSS and JavaScript to show you how computers understand language')
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for word in doc:
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if word.dep_ in ('xcomp', 'ccomp'):
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print(''.join(w.text_with_ws for w in word.subtree))
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# It'd probably be better for `word.subtree` to return a `Span` object instead
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# of a generator over the tokens. If you want the `Span` you can get it via the
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# `.right_edge` and `.left_edge` properties. The `Span` object is nice because
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# you can easily get a vector, merge it, etc.
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doc = nlp(u'displaCy uses CSS and JavaScript to show you how computers understand language')
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for word in doc:
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if word.dep_ in ('xcomp', 'ccomp'):
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subtree_span = doc[word.left_edge.i : word.right_edge.i + 1]
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print(subtree_span.text, '|', subtree_span.root.text)
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print(subtree_span.similarity(doc))
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print(subtree_span.similarity(subtree_span.root))
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# You might also want to select a head, and then select a start and end position by
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# walking along its children. You could then take the `.left_edge` and `.right_edge`
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# of those tokens, and use it to calculate a span.
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