spaCy/website/docs/usage/101/_pos-deps.md
2020-09-03 13:13:03 +02:00

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After tokenization, spaCy can parse and tag a given Doc. This is where the trained pipeline and its statistical models come in, which enable spaCy to make predictions of which tag or label most likely applies in this context. A trained component includes binary data that is produced by showing a system enough examples for it to make predictions that generalize across the language for example, a word following "the" in English is most likely a noun.

Linguistic annotations are available as Token attributes. Like many NLP libraries, spaCy 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 _ to its name:

### {executable="true"}
import spacy

nlp = spacy.load("en_core_web_sm")
doc = nlp("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)
  • Text: The original word text.
  • Lemma: The base form of the word.
  • POS: The simple UPOS part-of-speech tag.
  • Tag: The detailed part-of-speech tag.
  • Dep: Syntactic dependency, i.e. the relation between tokens.
  • Shape: The word shape capitalization, punctuation, digits.
  • is alpha: Is the token an alpha character?
  • is stop: Is the token part of a stop list, i.e. the most common words of the language?
Text Lemma POS Tag Dep Shape alpha stop
Apple apple PROPN NNP nsubj Xxxxx True False
is be AUX VBZ aux xx True True
looking look VERB VBG ROOT xxxx True False
at at ADP IN prep xx True True
buying buy VERB VBG pcomp xxxx True False
U.K. u.k. PROPN NNP compound X.X. False False
startup startup NOUN NN dobj xxxx True False
for for ADP IN prep xxx True True
$ $ SYM $ quantmod $ False False
1 1 NUM CD compound d False False
billion billion NUM CD pobj xxxx True False

Tip: Understanding tags and labels

Most of the tags and labels look pretty abstract, and they vary between languages. spacy.explain will show you a short description for example, spacy.explain("VBZ") returns "verb, 3rd person singular present".

Using spaCy's built-in displaCy visualizer, here's what our example sentence and its dependencies look like:

import DisplaCyLongHtml from 'images/displacy-long.html'; import { Iframe } from 'components/embed'