spaCy/website/docs/usage/101/_pos-deps.md

After tokenization, spaCy can parse and tag a given Doc. This is where the statistical model comes in, which enables spaCy to 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 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 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	VERB	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'