Rooba
/
spaCy
mirror of https://github.com/explosion/spaCy.git
1
1
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Change example title to Dr. 

Change example title to Dr. so the current model does exclude the title
in the initial example.
This commit is contained in:
Adriane Boyd 2020-06-16 20:36:21 +02:00
parent a6abdfbc3c
commit f0fd77648f
1 changed files with 6 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
website/docs/usage/rule-based-matching.md
View File

@ -1158,17 +1158,17 @@ what you need for your application.
> available corpus.
For example, the corpus spaCy's [English models](/models/en) were trained on
defines a `PERSON` entity as just the **person name**, without titles like "Mr"
or "Dr". This makes sense, because it makes it easier to resolve the entity type
back to a knowledge base. But what if your application needs the full names,
_including_ the titles?
defines a `PERSON` entity as just the **person name**, without titles like "Mr."
or "Dr.". This makes sense, because it makes it easier to resolve the entity
type back to a knowledge base. But what if your application needs the full
names, _including_ the titles?
```python
### {executable="true"}
import spacy
nlp = spacy.load("en_core_web_sm")
doc = nlp("Dr Alex Smith chaired first board meeting of Acme Corp Inc.")
doc = nlp("Dr. Alex Smith chaired first board meeting of Acme Corp Inc.")
print([(ent.text, ent.label_) for ent in doc.ents])
```
@ -1233,7 +1233,7 @@ def expand_person_entities(doc):
# Add the component after the named entity recognizer
nlp.add_pipe(expand_person_entities, after='ner')
doc = nlp("Dr Alex Smith chaired first board meeting of Acme Corp Inc.")
doc = nlp("Dr. Alex Smith chaired first board meeting of Acme Corp Inc.")
print([(ent.text, ent.label_) for ent in doc.ents])
```