Skip to main content

Trained Korean Lemmatizer

Project description

한국어 용언 분석기 (Korean Lemmatizer)

한국어의 동사와 형용사의 활용형 (surfacial form) 을 분석합니다. 한국어 용언 분석기는 다음의 기능을 제공합니다.

  1. 입력된 단어를 어간 (stem) 과 어미 (eomi) 으로 분리
  2. 입력된 단어를 원형으로 복원

이 패키지의 구현 원리는 github.io 블로그에 정리하였습니다.

Usage

analyze, lemmatize, conjugate

analyze function returns morphemes of the given predicator word

from soylemma import Lemmatizer

lemmatizer = Lemmatizer()
lemmatizer.analyze('차가우니까')

The return value forms list of tuples because there can be more than one morpheme combination.

[(('차갑', 'Adjective'), ('우니까', 'Eomi'))]

lemmatize function returns lemma of the given predicator word.

lemmatizer.lemmatize('차가우니까')
[('차갑다', 'Adjective')]

If the input word is not predicator such as Noun, it return empty list.

lemmatizer.lemmatize('한국어') # []

conjugate function returns surfacial form. You should put stem and eomi as arguments. It returns all possible surfacial forms for the given stem and eomi.

lemmatizer.conjugate(stem='차갑', eomi='우니까')
lemmatizer.conjugate('예쁘', '었던')
['차가우니까', '차갑우니까']
['예뻤던', '예쁘었던']

update dictionaries and rules

For demonstration, we use dictioanry demo.

어여뻤어 cannot be analyzed because the adjective 어여쁘 does not enrolled in dictionary.

from soylemma import Lemmatizer

lemmatizer = Lemmatizer(dictionary_name='demo')
print(lemmatizer.analyze('어여뻤어')) # []

So, we add the word with tag using add_words function. Do it again. Then you can see the word 어여뻤어 is analyzed.

lemmatizer.add_words('어여쁘', 'Adjective')
lemmatizer.analyze('어여뻤어')
[(('어여쁘', 'Adjective'), ('었어', 'Eomi'))]

However, the word 파랬다 is still not able to be analyzed because the lemmatization rule for surfacial form does not exist.

lemmatizer.analyze('파랬다') # []

So, in this time, we update additional lemmatization rules using add_lemma_rules function.

supplements = {
    '랬': {('랗', '았')}
}

lemmatizer.add_lemma_rules(supplements)

After that, we can see the word 파랬다 is analyzed, and also conjugation of 파랗 + 았다 is available.

lemmatizer.analyze('파랬다')
lemmatizer.conjugate('파랗', '았다')
[(('파랗', 'Adjective'), ('았다', 'Eomi'))]
['파랬다', '파랗았다']

debug on

If you wonder which subwords came up as candidates of (stem, eomi), use debug.

lemmatizer.analyze('파랬다', debug=True)
[DEBUG] word: 파랬다 = 파랗 + 았다, conjugation: 랬 = 랗 + 았
[(('파랗', 'Adjective'), ('았다', 'Eomi'))]

lemmatization rule extractor

You can extract lemmatization rule using extract_rule function.

from soylemma import extract_rule

eojeol = '로드무비였다'
lw = '로드무비이'
lt = 'Adjective'
rw = '었다'
rt = 'Eomi'

extract_rule(eojeol, lw, lt, rw, rt)
('였다', ('이', '었다'))

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

soylemma-0.2.0.tar.gz (125.2 kB view details)

Uploaded Source

Built Distributions

soylemma-0.2.0-py3.7.egg (135.0 kB view details)

Uploaded Source

soylemma-0.2.0-py3-none-any.whl (124.1 kB view details)

Uploaded Python 3

File details

Details for the file soylemma-0.2.0.tar.gz.

File metadata

  • Download URL: soylemma-0.2.0.tar.gz
  • Upload date:
  • Size: 125.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.4.2 requests/2.21.0 setuptools/40.6.3 requests-toolbelt/0.9.1 tqdm/4.28.1 CPython/3.7.1

File hashes

Hashes for soylemma-0.2.0.tar.gz
Algorithm Hash digest
SHA256 fa43afd27be7059fe4716774c928454f091e3a3b686a1f97c12e3d825960b6ec
MD5 08dd559b9b04ceb9755fa55e140924e8
BLAKE2b-256 bf9a6fd9d5f475191aa834f8c75c5a94759fab5ebfc240e22262da43f006cbff

See more details on using hashes here.

File details

Details for the file soylemma-0.2.0-py3.7.egg.

File metadata

  • Download URL: soylemma-0.2.0-py3.7.egg
  • Upload date:
  • Size: 135.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.4.2 requests/2.21.0 setuptools/40.6.3 requests-toolbelt/0.9.1 tqdm/4.28.1 CPython/3.7.1

File hashes

Hashes for soylemma-0.2.0-py3.7.egg
Algorithm Hash digest
SHA256 8b2d4734c9b7669ae95810ff561acfd1ed0a86b26f1307cb1e777412196ded6c
MD5 ed009d4e518b9a9283f54c9cfabea4d5
BLAKE2b-256 124fc2b97e5407d843f1aac8f7250fb5253ae788889972ff647089015d429b55

See more details on using hashes here.

File details

Details for the file soylemma-0.2.0-py3-none-any.whl.

File metadata

  • Download URL: soylemma-0.2.0-py3-none-any.whl
  • Upload date:
  • Size: 124.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.4.2 requests/2.21.0 setuptools/40.6.3 requests-toolbelt/0.9.1 tqdm/4.28.1 CPython/3.7.1

File hashes

Hashes for soylemma-0.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 6767c3e35a45a475bf7635029eaa20ac5f492574b73952d1b6bfb78ed23a3a69
MD5 540d1ebce5f64e47791dae7fa6446ea9
BLAKE2b-256 03f5d612c52b363a1e04b2df6dbde86bedd84035ba3d07651c586dcfdac50b53

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page