mirror of
https://github.com/explosion/spaCy.git
synced 2025-01-01 12:56:29 +03:00
0b4b4f1819
* document token ent_kb_id * document span kb_id * update pipeline documentation * prior and context weights as bool's instead * entitylinker api documentation * drop for both models * finish entitylinker documentation * small fixes * documentation for KB * candidate documentation * links to api pages in code * small fix * frequency examples as counts for consistency * consistent documentation about tensors returned by predict * add entity linking to usage 101 * add entity linking infobox and KB section to 101 * entity-linking in linguistic features * small typo corrections * training example and docs for entity_linker * predefined nlp and kb * revert back to similarity encodings for simplicity (for now) * set prior probabilities to 0 when excluded * code clean up * bugfix: deleting kb ID from tokens when entities were removed * refactor train el example to use either model or vocab * pretrain_kb example for example kb generation * add to training docs for KB + EL example scripts * small fixes * error numbering * ensure the language of vocab and nlp stay consistent across serialization * equality with = * avoid conflict in errors file * add error 151 * final adjustements to the train scripts - consistency * update of goldparse documentation * small corrections * push commit * typo fix * add candidate API to kb documentation * update API sidebar with EntityLinker and KnowledgeBase * remove EL from 101 docs * remove entity linker from 101 pipelines / rephrase * custom el model instead of existing model * set version to 2.2 for EL functionality * update documentation for 2 CLI scripts
39 lines
1.7 KiB
Markdown
39 lines
1.7 KiB
Markdown
A named entity is a "real-world object" that's assigned a name – for example, a
|
||
person, a country, a product or a book title. spaCy can **recognize**
|
||
[various types](/api/annotation#named-entities) of named entities in a document,
|
||
by asking the model for a **prediction**. Because models are statistical and
|
||
strongly depend on the examples they were trained on, this doesn't always work
|
||
_perfectly_ and might need some tuning later, depending on your use case.
|
||
|
||
Named entities are available as the `ents` property of a `Doc`:
|
||
|
||
```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 ent in doc.ents:
|
||
print(ent.text, ent.start_char, ent.end_char, ent.label_)
|
||
```
|
||
|
||
> - **Text:** The original entity text.
|
||
> - **Start:** Index of start of entity in the `Doc`.
|
||
> - **End:** Index of end of entity in the `Doc`.
|
||
> - **Label:** Entity label, i.e. type.
|
||
|
||
| Text | Start | End | Label | Description |
|
||
| ----------- | :---: | :-: | ------- | ---------------------------------------------------- |
|
||
| Apple | 0 | 5 | `ORG` | Companies, agencies, institutions. |
|
||
| U.K. | 27 | 31 | `GPE` | Geopolitical entity, i.e. countries, cities, states. |
|
||
| \$1 billion | 44 | 54 | `MONEY` | Monetary values, including unit. |
|
||
|
||
Using spaCy's built-in [displaCy visualizer](/usage/visualizers), here's what
|
||
our example sentence and its named entities look like:
|
||
|
||
import DisplaCyEntHtml from 'images/displacy-ent1.html'; import { Iframe } from
|
||
'components/embed'
|
||
|
||
<Iframe title="displaCy visualization of entities" html={DisplaCyEntHtml} height={100} />
|