spaCy/website/usage/_spacy-101/_pos-deps.jade

//- 💫 DOCS > USAGE > SPACY 101 > POS TAGGING AND DEPENDENCY PARSING

p
    |  After tokenization, spaCy can #[strong parse] and #[strong tag] a
    |  given #[code Doc]. This is where the statistical model comes in, which
    |  enables spaCy to #[strong make a prediction] of which tag or label most
    |  likely applies in this context. A model consists of binary data and is
    |  produced by showing a system enough examples for it to make predictions
    |  that generalise across the language – for example, a word following "the"
    |  in English is most likely a noun.

p
    |  Linguistic annotations are available as
    |  #[+api("token#attributes") #[code Token] attributes]. Like many NLP
    |  libraries, spaCy #[strong encodes all strings to hash values] to reduce
    |  memory usage and improve efficiency. So to get the readable string
    |  representation of an attribute, we need to add an underscore #[code _]
    |  to its name:

+code-exec.
    import spacy

    nlp = spacy.load('en_core_web_sm')
    doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')

    for token in doc:
        print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_,
              token.shape_, token.is_alpha, token.is_stop)

+aside
    |  #[strong Text:] The original word text.#[br]
    |  #[strong Lemma:] The base form of the word.#[br]
    |  #[strong POS:] The simple part-of-speech tag.#[br]
    |  #[strong Tag:] The detailed part-of-speech tag.#[br]
    |  #[strong Dep:] Syntactic dependency, i.e. the relation between tokens.#[br]
    |  #[strong Shape:] The word shape – capitalisation, punctuation, digits.#[br]
    |  #[strong is alpha:] Is the token an alpha character?#[br]
    |  #[strong is stop:] Is the token part of a stop list, i.e. the most common
    |  words of the language?#[br]

+table(["Text", "Lemma", "POS", "Tag", "Dep", "Shape", "alpha", "stop"])
    - var style = [0, 0, 1, 1, 1, 1, 1, 1]
    +annotation-row(["Apple", "apple", "PROPN", "NNP", "nsubj", "Xxxxx", true, false], style)
    +annotation-row(["is", "be", "VERB", "VBZ", "aux", "xx", true, true], style)
    +annotation-row(["looking", "look", "VERB", "VBG", "ROOT", "xxxx", true, false], style)
    +annotation-row(["at", "at", "ADP", "IN", "prep", "xx", true, true], style)
    +annotation-row(["buying", "buy", "VERB", "VBG", "pcomp", "xxxx", true, false], style)
    +annotation-row(["U.K.", "u.k.", "PROPN", "NNP", "compound", "X.X.", false, false], style)
    +annotation-row(["startup", "startup", "NOUN", "NN", "dobj", "xxxx", true, false], style)
    +annotation-row(["for", "for", "ADP", "IN", "prep", "xxx", true, true], style)
    +annotation-row(["$", "$", "SYM", "$", "quantmod", "$", false, false], style)
    +annotation-row(["1", "1", "NUM", "CD", "compound", "d", false, false], style)
    +annotation-row(["billion", "billion", "NUM", "CD", "pobj", "xxxx", true, false], style)

+aside("Tip: Understanding tags and labels")
    |  Most of the tags and labels look pretty abstract, and they vary between
    |  languages. #[code spacy.explain()] will show you a short description –
    |  for example, #[code spacy.explain("VBZ")] returns "verb, 3rd person
    |  singular present".

p
    |  Using spaCy's built-in #[+a("/usage/visualizers") displaCy visualizer],
    |  here's what our example sentence and its dependencies look like:

+codepen("030d1e4dfa6256cad8fdd59e6aefecbe", 460)