mirror of https://github.com/explosion/spaCy.git
63 lines
3.1 KiB
Markdown
63 lines
3.1 KiB
Markdown
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` | `probj` | `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} />
|