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

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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](/api/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:
```python
### {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](/usage/visualizers), here's what
our example sentence and its dependencies look like:
import DisplaCyLongHtml from 'images/displacy-long.html'; import { Iframe } from
'components/embed'
<Iframe title="displaCy visualization of dependencies and entities" html={DisplaCyLongHtml} height={450} />