Rule-based facts extraction for Russian language


Keywords
natural, language, processing, russian, morphology, glr, parser, earley-parser, information-extraction, nlp, python, tomita, tomita-parser
License
MIT
Install
pip install yargy==0.15.0

Documentation

CI

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 yargy

Usage

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

Development

Dev env

pyenv virtualenv 3.11.0 natasha-yargy
pyenv activate natasha-yargy

pip install -r requirements/dev.txt
pip install -e .

pyenv virtualenv-delete natasha-yargy

Test + lint

make test

Update docs

make exec-docs

# Manually check git diff docs/, commit

Release

# Update setup.py version

git commit -am 'Up version'
git tag v0.15.1

git push
git push --tags

# Github Action builds dist and publishes to PyPi