Yargy uses rules and dictionaries to extract structured information from Russian texts. Yargy is similar to Tomita parser.
Install
Yargy supports Python 3.7+, PyPy 3, depends only on Pymorphy2.
$ pip install yargyUsage
from yargy import Parser, rule, and_, not_
from yargy.interpretation import fact
from yargy.predicates import gram
from yargy.relations import gnc_relation
from yargy.pipelines import morph_pipeline
Name = fact(
'Name',
['first', 'last'],
)
Person = fact(
'Person',
['position', 'name']
)
LAST = and_(
gram('Surn'),
not_(gram('Abbr')),
)
FIRST = and_(
gram('Name'),
not_(gram('Abbr')),
)
POSITION = morph_pipeline([
'управляющий директор',
'вице-мэр'
])
gnc = gnc_relation()
NAME = rule(
FIRST.interpretation(
Name.first
).match(gnc),
LAST.interpretation(
Name.last
).match(gnc)
).interpretation(
Name
)
PERSON = rule(
POSITION.interpretation(
Person.position
).match(gnc),
NAME.interpretation(
Person.name
)
).interpretation(
Person
)
parser = Parser(PERSON)
match = parser.match('управляющий директор Иван Ульянов')
print(match)
Person(
position='управляющий директор',
name=Name(
first='Иван',
last='Ульянов'
)
)Documentation
All materials are in Russian:
Support
- Chat — https://t.me/natural_language_processing
- Issues — https://github.com/natasha/yargy/issues
- Commercial support — https://lab.alexkuk.ru
Development
Dev env
brew install graphviz
python -m venv ~/.venvs/natasha-yargy
source ~/.venvs/natasha-yargy/bin/activate
pip install -r requirements/dev.txt
pip install -e .
python -m ipykernel install --user --name natasha-yargyTest + lint
make testUpdate docs
make exec-docs
# Manually check git diff docs/, commitRelease
# Update setup.py version
git commit -am 'Up version'
git tag v0.16.0
git push
git push --tags
# Github Action builds dist and publishes to PyPi