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68 lines
2.5 KiB
Python
68 lines
2.5 KiB
Python
#!/usr/bin/env python
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# coding: utf8
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"""This example shows how to navigate the parse tree including subtrees
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attached to a word.
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Based on issue #252:
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"In the documents and tutorials the main thing I haven't found is
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examples on how to break sentences down into small sub thoughts/chunks. The
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noun_chunks is handy, but having examples on using the token.head to find small
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(near-complete) sentence chunks would be neat. Lets take the example sentence:
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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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show you how computers understand [language]
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I'm assuming that we can use the token.head to build these groups."
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Compatible with: spaCy v2.0.0+
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Last tested with: v2.1.0
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"""
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from __future__ import unicode_literals, print_function
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import plac
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import spacy
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@plac.annotations(model=("Model to load", "positional", None, str))
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def main(model="en_core_web_sm"):
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nlp = spacy.load(model)
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print("Loaded model '%s'" % model)
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doc = nlp(
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"displaCy uses CSS and JavaScript to show you how computers "
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"understand language"
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)
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# The easiest way is to find the head of the subtree you want, and then use
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# the `.subtree`, `.children`, `.lefts` and `.rights` iterators. `.subtree`
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# is the one that does what you're asking for most directly:
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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
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# instead of a generator over the tokens. If you want the `Span` you can
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# get it via the `.right_edge` and `.left_edge` properties. The `Span`
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# object is nice because you can easily get a vector, merge it, etc.
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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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# You might also want to select a head, and then select a start and end
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# position by walking along its children. You could then take the
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# `.left_edge` and `.right_edge` of those tokens, and use it to calculate
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# a span.
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if __name__ == "__main__":
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plac.call(main)
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# Expected output:
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# to show you how computers understand language
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# how computers understand language
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# to show you how computers understand language | show
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# how computers understand language | understand
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