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Merge remote-tracking branch 'origin/master' into feature/improve-pretrain

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Matthw Honnibal 2019-11-17 17:19:34 +01:00
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website/docs/usage/rule-based-matching.md
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@ -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]) 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 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 models and enhance the named entity recognizer. If it's added **before the
`"ner"` component**, the entity recognizer will respect the existing entity `"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) 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} ### Using pattern files {#entityruler-files}
The [`to_disk`](/api/entityruler#to_disk) and The [`to_disk`](/api/entityruler#to_disk) and