mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-30 20:06:30 +03:00
235 lines
11 KiB
Plaintext
235 lines
11 KiB
Plaintext
//- 💫 DOCS > USAGE > LINGUISTIC FEATURES > DEPENDENCY PARSE
|
||
|
||
p
|
||
| spaCy features a fast and accurate syntactic dependency parser, and has
|
||
| a rich API for navigating the tree. The parser also powers the sentence
|
||
| boundary detection, and lets you iterate over base noun phrases, or
|
||
| "chunks". You can check whether a #[+api("doc") #[code Doc]] object has
|
||
| been parsed with the #[code doc.is_parsed] attribute, which returns a
|
||
| boolean value. If this attribute is #[code False], the default sentence
|
||
| iterator will raise an exception.
|
||
|
||
+h(3, "noun-chunks") Noun chunks
|
||
|
||
p
|
||
| Noun chunks are "base noun phrases" – flat phrases that have a noun as
|
||
| their head. You can think of noun chunks as a noun plus the words describing
|
||
| the noun – for example, "the lavish green grass" or "the world’s largest
|
||
| tech fund". To get the noun chunks in a document, simply iterate over
|
||
| #[+api("doc#noun_chunks") #[code Doc.noun_chunks]].
|
||
|
||
+code("Example").
|
||
nlp = spacy.load('en')
|
||
doc = nlp(u'Autonomous cars shift insurance liability toward manufacturers')
|
||
for chunk in doc.noun_chunks:
|
||
print(chunk.text, chunk.root.text, chunk.root.dep_,
|
||
chunk.root.head.text)
|
||
|
||
+aside
|
||
| #[strong Text:] The original noun chunk text.#[br]
|
||
| #[strong Root text:] The original text of the word connecting the noun
|
||
| chunk to the rest of the parse.#[br]
|
||
| #[strong Root dep:] Dependcy relation connecting the root to its head.#[br]
|
||
| #[strong Root head text:] The text of the root token's head.#[br]
|
||
|
||
+table(["Text", "root.text", "root.dep_", "root.head.text"])
|
||
- var style = [0, 0, 1, 0]
|
||
+annotation-row(["Autonomous cars", "cars", "nsubj", "shift"], style)
|
||
+annotation-row(["insurance liability", "liability", "dobj", "shift"], style)
|
||
+annotation-row(["manufacturers", "manufacturers", "pobj", "toward"], style)
|
||
|
||
+h(3, "navigating") Navigating the parse tree
|
||
|
||
p
|
||
| spaCy uses the terms #[strong head] and #[strong child] to describe the words
|
||
| #[strong connected by a single arc] in the dependency tree. The term
|
||
| #[strong dep] is used for the arc label, which describes the type of
|
||
| syntactic relation that connects the child to the head. As with other
|
||
| attributes, the value of #[code .dep] is a hash value. You can get
|
||
| the string value with #[code .dep_].
|
||
|
||
+code("Example").
|
||
doc = nlp(u'Autonomous cars shift insurance liability toward manufacturers')
|
||
for token in doc:
|
||
print(token.text, token.dep_, token.head.text, token.head.pos_,
|
||
[child for child in token.children])
|
||
|
||
+aside
|
||
| #[strong Text]: The original token text.#[br]
|
||
| #[strong Dep]: The syntactic relation connecting child to head.#[br]
|
||
| #[strong Head text]: The original text of the token head.#[br]
|
||
| #[strong Head POS]: The part-of-speech tag of the token head.#[br]
|
||
| #[strong Children]: The immediate syntactic dependents of the token.
|
||
|
||
+table(["Text", "Dep", "Head text", "Head POS", "Children"])
|
||
- var style = [0, 1, 0, 1, 0]
|
||
+annotation-row(["Autonomous", "amod", "cars", "NOUN", ""], style)
|
||
+annotation-row(["cars", "nsubj", "shift", "VERB", "Autonomous"], style)
|
||
+annotation-row(["shift", "ROOT", "shift", "VERB", "cars, liability"], style)
|
||
+annotation-row(["insurance", "compound", "liability", "NOUN", ""], style)
|
||
+annotation-row(["liability", "dobj", "shift", "VERB", "insurance, toward"], style)
|
||
+annotation-row(["toward", "prep", "liability", "NOUN", "manufacturers"], style)
|
||
+annotation-row(["manufacturers", "pobj", "toward", "ADP", ""], style)
|
||
|
||
+codepen("dcf8d293367ca185b935ed2ca11ebedd", 370)
|
||
|
||
p
|
||
| Because the syntactic relations form a tree, every word has
|
||
| #[strong exactly one head]. You can therefore iterate over the arcs in
|
||
| the tree by iterating over the words in the sentence. This is usually
|
||
| the best way to match an arc of interest — from below:
|
||
|
||
+code.
|
||
from spacy.symbols import nsubj, VERB
|
||
|
||
# Finding a verb with a subject from below — good
|
||
verbs = set()
|
||
for possible_subject in doc:
|
||
if possible_subject.dep == nsubj and possible_subject.head.pos == VERB:
|
||
verbs.add(possible_subject.head)
|
||
|
||
p
|
||
| If you try to match from above, you'll have to iterate twice: once for
|
||
| the head, and then again through the children:
|
||
|
||
+code.
|
||
# Finding a verb with a subject from above — less good
|
||
verbs = []
|
||
for possible_verb in doc:
|
||
if possible_verb.pos == VERB:
|
||
for possible_subject in possible_verb.children:
|
||
if possible_subject.dep == nsubj:
|
||
verbs.append(possible_verb)
|
||
break
|
||
|
||
p
|
||
| To iterate through the children, use the #[code token.children]
|
||
| attribute, which provides a sequence of #[+api("token") #[code Token]]
|
||
| objects.
|
||
|
||
+h(4, "navigating-around") Iterating around the local tree
|
||
|
||
p
|
||
| A few more convenience attributes are provided for iterating around the
|
||
| local tree from the token. The #[code .lefts] and #[code .rights]
|
||
| attributes provide sequences of syntactic children that occur before and
|
||
| after the token. Both sequences are in sentences order. There are also
|
||
| two integer-typed attributes, #[code .n_rights] and #[code .n_lefts],
|
||
| that give the number of left and right children.
|
||
|
||
+code.
|
||
doc = nlp(u'bright red apples on the tree')
|
||
assert [token.text for token in doc[2].lefts]) == [u'bright', u'red']
|
||
assert [token.text for token in doc[2].rights]) == ['on']
|
||
assert doc[2].n_lefts == 2
|
||
assert doc[2].n_rights == 1
|
||
|
||
p
|
||
| You can get a whole phrase by its syntactic head using the
|
||
| #[code .subtree] attribute. This returns an ordered sequence of tokens.
|
||
| You can walk up the tree with the #[code .ancestors] attribute, and
|
||
| check dominance with the #[+api("token#is_ancestor") #[code .is_ancestor()]]
|
||
| method.
|
||
|
||
+aside("Projective vs. non-projective")
|
||
| For the #[+a("/models/en") default English model], the
|
||
| parse tree is #[strong projective], which means that there are no crossing
|
||
| brackets. The tokens returned by #[code .subtree] are therefore guaranteed
|
||
| to be contiguous. This is not true for the German model, which has many
|
||
| #[+a(COMPANY_URL + "/blog/german-model#word-order", true) non-projective dependencies].
|
||
|
||
+code.
|
||
doc = nlp(u'Credit and mortgage account holders must submit their requests')
|
||
root = [token for token in doc if token.head is token][0]
|
||
subject = list(root.lefts)[0]
|
||
for descendant in subject.subtree:
|
||
assert subject.is_ancestor(descendant)
|
||
print(descendant.text, descendant.dep_, descendant.n_lefts, descendant.n_rights,
|
||
[ancestor.text for ancestor in descendant.ancestors])
|
||
|
||
+table(["Text", "Dep", "n_lefts", "n_rights", "ancestors"])
|
||
- var style = [0, 1, 1, 1, 0]
|
||
+annotation-row(["Credit", "nmod", 0, 2, "holders, submit"], style)
|
||
+annotation-row(["and", "cc", 0, 0, "Credit, holders, submit"], style)
|
||
+annotation-row(["mortgage", "compound", 0, 0, "account, Credit, holders, submit"], style)
|
||
+annotation-row(["account", "conj", 1, 0, "Credit, holders, submit"], style)
|
||
+annotation-row(["holders", "nsubj", 1, 0, "submit"], style)
|
||
|
||
p
|
||
| Finally, the #[code .left_edge] and #[code .right_edge] attributes
|
||
| can be especially useful, because they give you the first and last token
|
||
| of the subtree. This is the easiest way to create a #[code Span] object
|
||
| for a syntactic phrase. Note that #[code .right_edge] gives a token
|
||
| #[strong within] the subtree — so if you use it as the end-point of a
|
||
| range, don't forget to #[code +1]!
|
||
|
||
+code.
|
||
doc = nlp(u'Credit and mortgage account holders must submit their requests')
|
||
span = doc[doc[4].left_edge.i : doc[4].right_edge.i+1]
|
||
span.merge()
|
||
for token in doc:
|
||
print(token.text, token.pos_, token.dep_, token.head.text)
|
||
|
||
+table(["Text", "POS", "Dep", "Head text"])
|
||
- var style = [0, 1, 1, 0]
|
||
+annotation-row(["Credit and mortgage account holders", "NOUN", "nsubj", "submit"], style)
|
||
+annotation-row(["must", "VERB", "aux", "submit"], style)
|
||
+annotation-row(["submit", "VERB", "ROOT", "submit"], style)
|
||
+annotation-row(["their", "ADJ", "poss", "requests"], style)
|
||
+annotation-row(["requests", "NOUN", "dobj", "submit"], style)
|
||
|
||
+h(3, "displacy") Visualizing dependencies
|
||
|
||
p
|
||
| The best way to understand spaCy's dependency parser is interactively.
|
||
| To make this easier, spaCy v2.0+ comes with a visualization module. Simply
|
||
| pass a #[code Doc] or a list of #[code Doc] objects to
|
||
| displaCy and run #[+api("displacy#serve") #[code displacy.serve]] to
|
||
| run the web server, or #[+api("displacy#render") #[code displacy.render]]
|
||
| to generate the raw markup. If you want to know how to write rules that
|
||
| hook into some type of syntactic construction, just plug the sentence into
|
||
| the visualizer and see how spaCy annotates it.
|
||
|
||
+code.
|
||
from spacy import displacy
|
||
|
||
doc = nlp(u'Autonomous cars shift insurance liability toward manufacturers')
|
||
displacy.serve(doc, style='dep')
|
||
|
||
+infobox
|
||
| For more details and examples, see the
|
||
| #[+a("/usage/visualizers") usage guide on visualizing spaCy]. You
|
||
| can also test displaCy in our #[+a(DEMOS_URL + "/displacy", true) online demo].
|
||
|
||
+h(3, "disabling") Disabling the parser
|
||
|
||
p
|
||
| In the #[+a("/models") default models], the parser is loaded and enabled
|
||
| as part of the
|
||
| #[+a("docs/usage/language-processing-pipelines") standard processing pipeline].
|
||
| If you don't need any of the syntactic information, you should disable
|
||
| the parser. Disabling the parser will make spaCy load and run much faster.
|
||
| If you want to load the parser, but need to disable it for specific
|
||
| documents, you can also control its use on the #[code nlp] object.
|
||
|
||
+code.
|
||
nlp = spacy.load('en', disable=['parser'])
|
||
nlp = English().from_disk('/model', disable=['parser'])
|
||
doc = nlp(u"I don't want parsed", disable=['parser'])
|
||
|
||
+infobox("Important note: disabling pipeline components")
|
||
.o-block
|
||
| Since spaCy v2.0 comes with better support for customising the
|
||
| processing pipeline components, the #[code parser] keyword argument
|
||
| has been replaced with #[code disable], which takes a list of
|
||
| #[+a("/usage/processing-pipelines") pipeline component names].
|
||
| This lets you disable both default and custom components when loading
|
||
| a model, or initialising a Language class via
|
||
| #[+api("language-from_disk") #[code from_disk]].
|
||
+code-new.
|
||
nlp = spacy.load('en', disable=['parser'])
|
||
doc = nlp(u"I don't want parsed", disable=['parser'])
|
||
+code-old.
|
||
nlp = spacy.load('en', parser=False)
|
||
doc = nlp(u"I don't want parsed", parse=False)
|