//- 💫 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:] Dependency 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 #[+api("token#lefts") #[code Token.lefts]]
    |  and #[+api("token#rights") #[code Token.rights]] attributes provide
    |  sequences of syntactic children that occur before and after the token.
    |  Both sequences are in sentence order. There are also two integer-typed
    |  attributes, #[+api("token#n_rights") #[code Token.n_rights]] and
    |  #[+api("token#n_lefts") #[code Token.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
    |  #[+api("token#subtree") #[code Token.subtree]] attribute. This returns an
    |  ordered  sequence of tokens. You can walk up the tree with the
    |  #[+api("token#ancestors") #[code Token.ancestors]] attribute, and
    |  check dominance with
    |  #[+api("token#is_ancestor") #[code Token.is_ancestor()]].

+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 == token][0]
    subject = list(root.lefts)[0]
    for descendant in subject.subtree:
        assert subject is descendant or 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)

+infobox("Dependency label scheme", "📖")
    |  For a list of the syntactic dependency labels assigned by spaCy's models
    |  across different languages, see the
    |  #[+a("/api/annotation#pos-tagging") dependency label scheme documentation].


+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("top-level#displacy.serve") #[code displacy.serve]] to
    |  run the web server, or #[+api("top-level#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("/usage/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)