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165 lines
6.3 KiB
Plaintext
165 lines
6.3 KiB
Plaintext
//- 💫 DOCS > USAGE > DEPENDENCY PARSE
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include ../../_includes/_mixins
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p
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| spaCy features a fast and accurate syntactic dependency parser, and has
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| a rich API for navigating the tree. The parser also powers the sentence
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| boundary detection, and lets you iterate over base noun phrases, or
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| "chunks".
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+aside-code("Example").
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import spacy
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nlp = spacy.load('en')
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doc = nlp(u'I like green eggs and ham.')
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for np in doc.noun_chunks:
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print(np.text, np.root.text, np.root.dep_, np.root.head.text)
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# I I nsubj like
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# green eggs eggs dobj like
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# ham ham conj eggs
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p
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| You can check whether a #[+api("doc") #[code Doc]] object has been
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| parsed with the #[code doc.is_parsed] attribute, which returns a boolean
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| value. If this attribute is #[code False], the default sentence iterator
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| will raise an exception.
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+h(2, "displacy") The displaCy visualizer
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p
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| The best way to understand spaCy's dependency parser is interactively,
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| through the #[+a(DEMOS_URL + "/displacy", true) displaCy visualizer]. If
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| you want to know how to write rules that hook into some type of syntactic
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| construction, just plug the sentence into the visualizer and see how
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| spaCy annotates it.
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+h(2, "navigating") Navigating the parse tree
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p
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| spaCy uses the terms #[em head] and #[em child] to describe the words
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| connected by a single arc in the dependency tree. The term #[em dep] is
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| used for the arc label, which describes the type of syntactic relation
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| that connects the child to the head. As with other attributes, the value
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| of #[code token.dep] is an integer. You can get the string value with
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| #[code token.dep_].
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+aside-code("Example").
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from spacy.symbols import det
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the, dog = nlp(u'the dog')
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assert the.dep == det
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assert the.dep_ == 'det'
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p
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| Because the syntactic relations form a tree, every word has exactly one
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| head. You can therefore iterate over the arcs in the tree by iterating
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| over the words in the sentence. This is usually the best way to match an
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| arc of interest — from below:
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+code.
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from spacy.symbols import nsubj, VERB
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# Finding a verb with a subject from below — good
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verbs = set()
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for possible_subject in doc:
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if possible_subject.dep == nsubj and possible_subject.head.pos == VERB:
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verbs.add(possible_subject.head)
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p
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| If you try to match from above, you'll have to iterate twice: once for
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| the head, and then again through the children:
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+code.
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# Finding a verb with a subject from above — less good
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verbs = []
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for possible_verb in doc:
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if possible_verb.pos == VERB:
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for possible_subject in possible_verb.children:
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if possible_subject.dep == nsubj:
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verbs.append(possible_verb)
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break
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p
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| To iterate through the children, use the #[code token.children]
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| attribute, which provides a sequence of #[+api("token") #[code Token]]
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| objects.
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p
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| A few more convenience attributes are provided for iterating around the
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| local tree from the token. The #[code .lefts] and #[code .rights]
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| attributes provide sequences of syntactic children that occur before and
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| after the token. Both sequences are in sentences order. There are also
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| two integer-typed attributes, #[code .n_rights] and #[code .n_lefts],
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| that give the number of left and right children.
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+aside-code("Examples").
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apples = nlp(u'bright red apples on the tree')[2]
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print([w.text for w in apples.lefts])
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# ['bright', 'red']
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print([w.text for w in apples.rights])
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# ['on']
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assert apples.n_lefts == 2
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assert apples.n_rights == 1
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from spacy.symbols import nsubj
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doc = nlp(u'Credit and mortgage account holders must submit their requests within 30 days.')
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root = [w for w in doc if w.head is w][0]
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subject = list(root.lefts)[0]
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for descendant in subject.subtree:
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assert subject.is_ancestor_of(descendant)
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from spacy.symbols import nsubj
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doc = nlp(u'Credit and mortgage account holders must submit their requests.')
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holders = doc[4]
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span = doc[holders.left_edge.i : holders.right_edge.i + 1]
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span.merge()
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for word in doc:
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print(word.text, word.pos_, word.dep_, word.head.text)
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# Credit and mortgage account holders nsubj NOUN submit
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# must VERB aux submit
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# submit VERB ROOT submit
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# their DET det requests
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# requests NOUN dobj submit
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p
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| You can get a whole phrase by its syntactic head using the
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| #[code .subtree] attribute. This returns an ordered sequence of tokens.
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| For the default English model, the parse tree is #[em projective], which
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| means that there are no crossing brackets. The tokens returned by
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| #[code .subtree] are therefore guaranteed to be contiguous. This is not
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| true for the German model, which has many
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| #[+a("https://explosion.ai/blog/german-model#word-order", true) non-projective dependencies].
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| You can walk up the tree with the #[code .ancestors] attribute, and
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| check dominance with the #[code .is_ancestor()] method.
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p
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| Finally, I often find the #[code .left_edge] and #[code right_edge]
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| attributes especially useful. They give you the first and last token
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| of the subtree. This is the easiest way to create a #[code Span] object
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| for a syntactic phrase — a useful operation.
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p
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| Note that #[code .right_edge] gives a token #[em within] the subtree —
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| so if you use it as the end-point of a range, don't forget to #[code +1]!
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+h(2, "disabling") Disabling the parser
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p
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| The parser is loaded and enabled by default. If you don't need any of
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| the syntactic information, you should disable the parser. Disabling the
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| parser will make spaCy load and run much faster. Here's how to prevent
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| the parser from being loaded:
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+code.
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import spacy
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nlp = spacy.load('en', parser=False)
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p
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| If you need to load the parser, but need to disable it for specific
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| documents, you can control its use with the #[code parse] keyword
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| argument:
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+code.
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nlp = spacy.load('en')
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doc1 = nlp(u'Text I do want parsed.')
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doc2 = nlp(u"Text I don't want parsed", parse=False)
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