explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2024-12-25 17:36:30 +03:00
Code Issues Packages Projects Releases Wiki Activity

Adjust order of docs sections [ci skip]

Browse Source
This commit is contained in:
Ines Montani 2019-11-17 16:08:56 +01:00
parent 74b951fe61
commit 5adcb352e9
1 changed files with 31 additions and 31 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

62
website/docs/usage/rule-based-matching.md
View File

@ -986,37 +986,6 @@ doc = nlp("Apple is opening its first big office in San Francisco.")
print([(ent.text, ent.label_) for ent in doc.ents])
```
### Adding IDs to patterns {#entityruler-ent-ids new="2.2.2"}
The [`EntityRuler`](/api/entityruler) can also accept an `id` attribute for each
pattern. Using the `id` attribute allows multiple patterns to be associated with
the same entity.
```python
### {executable="true"}
from spacy.lang.en import English
from spacy.pipeline import EntityRuler
nlp = English()
ruler = EntityRuler(nlp)
patterns = [{"label": "ORG", "pattern": "Apple", "id": "apple"},
{"label": "GPE", "pattern": [{"LOWER": "san"}, {"LOWER": "francisco"}], "id": "san-francisco"},
{"label": "GPE", "pattern": [{"LOWER": "san"}, {"LOWER": "fran"}], "id": "san-francisco"}]
ruler.add_patterns(patterns)
nlp.add_pipe(ruler)
doc1 = nlp("Apple is opening its first big office in San Francisco.")
print([(ent.text, ent.label_, ent.ent_id_) for ent in doc1.ents])
doc2 = nlp("Apple is opening its first big office in San Fran.")
print([(ent.text, ent.label_, ent.ent_id_) for ent in doc2.ents])
```
If the `id` attribute is included in the [`EntityRuler`](/api/entityruler)
patterns, the `ent_id_` property of the matched entity is set to the `id` given
in the patterns. So in the example above it's easy to identify that "San
Francisco" and "San Fran" are both the same entity.
The entity ruler is designed to integrate with spaCy's existing statistical
models and enhance the named entity recognizer. If it's added **before the
`"ner"` component**, the entity recognizer will respect the existing entity
@ -1051,6 +1020,37 @@ The `EntityRuler` can validate patterns against a JSON schema with the option
ruler = EntityRuler(nlp, validate=True)
```
### Adding IDs to patterns {#entityruler-ent-ids new="2.2.2"}
The [`EntityRuler`](/api/entityruler) can also accept an `id` attribute for each
pattern. Using the `id` attribute allows multiple patterns to be associated with
the same entity.
```python
### {executable="true"}
from spacy.lang.en import English
from spacy.pipeline import EntityRuler
nlp = English()
ruler = EntityRuler(nlp)
patterns = [{"label": "ORG", "pattern": "Apple", "id": "apple"},
{"label": "GPE", "pattern": [{"LOWER": "san"}, {"LOWER": "francisco"}], "id": "san-francisco"},
{"label": "GPE", "pattern": [{"LOWER": "san"}, {"LOWER": "fran"}], "id": "san-francisco"}]
ruler.add_patterns(patterns)
nlp.add_pipe(ruler)
doc1 = nlp("Apple is opening its first big office in San Francisco.")
print([(ent.text, ent.label_, ent.ent_id_) for ent in doc1.ents])
doc2 = nlp("Apple is opening its first big office in San Fran.")
print([(ent.text, ent.label_, ent.ent_id_) for ent in doc2.ents])
```
If the `id` attribute is included in the [`EntityRuler`](/api/entityruler)
patterns, the `ent_id_` property of the matched entity is set to the `id` given
in the patterns. So in the example above it's easy to identify that "San
Francisco" and "San Fran" are both the same entity.
### Using pattern files {#entityruler-files}
The [`to_disk`](/api/entityruler#to_disk) and