mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-29 11:26:28 +03:00
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(u'Apple is looking at buying U.K. startup for $1 billion')
|
|||
|
|
|
|||
|
|
for token in doc:
|
|||
|
|
print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_,
|
|||
|
|
token.shape_, token.is_alpha, token.is_stop)
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
> - **Text:** The original word text.
|
|||
|
|
> - **Lemma:** The base form of the word.
|
|||
|
|
> - **POS:** The simple part-of-speech tag.
|
|||
|
|
> - **Tag:** The detailed part-of-speech tag.
|
|||
|
|
> - **Dep:** Syntactic dependency, i.e. the relation between tokens.
|
|||
|
|
> - **Shape:** The word shape – capitalization, punctuation, digits.
|
|||
|
|
> - **is alpha:** Is the token an alpha character?
|
|||
|
|
> - **is stop:** Is the token part of a stop list, i.e. the most common words of
|
|||
|
|
> the language?
|
|||
|
|
|
|||
|
|
| Text | Lemma | POS | Tag | Dep | Shape | alpha | stop |
|
|||
|
|
| ------- | ------- | ------- | ----- | ---------- | ------- | ------- | ------- |
|
|||
|
|
| Apple | apple | `PROPN` | `NNP` | `nsubj` | `Xxxxx` | `True` | `False` |
|
|||
|
|
| is | be | `VERB` | `VBZ` | `aux` | `xx` | `True` | `True` |
|
|||
|
|
| looking | look | `VERB` | `VBG` | `ROOT` | `xxxx` | `True` | `False` |
|
|||
|
|
| at | at | `ADP` | `IN` | `prep` | `xx` | `True` | `True` |
|
|||
|
|
| buying | buy | `VERB` | `VBG` | `pcomp` | `xxxx` | `True` | `False` |
|
|||
|
|
| U.K. | u.k. | `PROPN` | `NNP` | `compound` | `X.X.` | `False` | `False` |
|
|||
|
|
| startup | startup | `NOUN` | `NN` | `dobj` | `xxxx` | `True` | `False` |
|
|||
|
|
| for | for | `ADP` | `IN` | `prep` | `xxx` | `True` | `True` |
|
|||
|
|
| \$ | \$ | `SYM` | `$` | `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} />
|