A curated list of resources dedicated to Natural Language Processing for Korean

한글, 한국어에 대한 자연어처리를 하는데에 유용한 자료 목록을 모아두었습니다.

Maintainers - Insik Kim

Thanks to Keon Kim, Martin Park for making awesome nlp, which is original awesome-nlp

Please feel free to pull requests, or email Insik Kim ([email protected]) to add links.

• Tutorials and Courses
  • videos
• Dataset
• Deep Learning for NLP
• Packages
  • Implemendations
  • Libraries
    • Python
    • C++
    • Scalar
  • Services
• Articles
  • Review Articles
  • Word Vectors
  • Thought Vectors
  • Machine Translation
  • General Natural Language Processing
  • Named Entity Recognition
  • Single Exchange Dialogs
  • Memory and Attention Models
  • General Natural Language Processing
  • Named Entity Recognition
  • Neural Network
  • Supplementary Materials
• Projects
• Blogs
• Credits

• Tensor Flow Tutorial on Seq2Seq Models
• Natural Language Understanding with Distributed Representation Lecture Note by Cho

• 세종말뭉치
• Wikipedia Korean
• Wiki Source Korean

출판물이기 때문에 문법 정보 추출에 도움이 되는 데이터

• 나무위키:데이터베이스 덤프, 나무 위키 DB Dump Download Mirror Site

미러에서 제공하는 7z파일을 이용하면 약 1.2GB 크기의 압축파일을 받을 수 있다.

위키 rawdata를 평문으로 바꿔주는 스크립트는 namu_wiki_db_preprocess 참고

• Pre-trained word embeddings for WSJ corpus by Koc AI-Lab
• Word2vec by Mikolov
• HLBL language model by Turian
• Real-valued vector "embeddings" by Dhillon
• Improving Word Representations Via Global Context And Multiple Word Prototypes by Huang
• Dependency based word embeddings
• Global Vectors for Word Representations

• Python - Python NLP Libraries

  • KoNLPy - A Python package for Korean natural language processing.

• C++ - C++ Libraries

  • Mecab (Korean) 형태소분석기

• Scalar - Scalar Libraries

  • twitter-korean-text 토큰 추출

https://github.com/twitter/twitter-korean-text

Resources about word vectors, aka word embeddings, and distributed representations for words.
Word vectors are numeric representations of words that are often used as input to deep learning systems. This process is sometimes called pretraining.

• word2vec 관련 이론 정리
• word2vec 튜토리얼

Thought vectors are numeric representations for sentences, paragraphs, and documents. The following papers are listed in order of date published, each one replaces the last as the state of the art in sentiment analysis.

• 시인 뉴럴. Multi-layer LSTM for character-level language models in Torch. implemented by Kim Tae Hoon.
• 한글 word2vec Demo. implemented by Daegeun Lee.

part of the lists are from

• ai-reading-list