├── .gitattributes ├── README.md ├── code ├── ch03-Preprocessing Korean.ipynb ├── ch04-FastText.ipynb ├── ch04-Skip Gram.ipynb ├── ch04-Word2Vec with Gensim.ipynb ├── ch05-Transformer.ipynb └── week07-ELMo │ ├── biLM.ipynb │ ├── frontend.ipynb │ └── modules │ ├── classify_layer.py │ ├── elmo.py │ ├── embedding_layer.py │ ├── encoder_base.py │ ├── highway.py │ ├── lstm_cell_with_projection.py │ ├── token_embedder.py │ └── util.py ├── data ├── corrected_ratings_corpus.txt ├── corrected_ratings_test.txt ├── corrected_ratings_train.txt ├── processed_korquad.txt ├── processed_ratings.txt ├── processed_ratings_test.txt ├── processed_ratings_train.txt ├── processed_review_movieid.txt ├── soyword.model └── space-correct.model ├── slide ├── ch01-Introduction.pdf ├── ch02-Vector in NLP.pdf ├── ch04-Word Embedding (GloVe, Swivel, Evaluation, Weighted Embedding).pdf ├── ch04-Word Embedding (NPLM, W2V, FastText).PDF ├── ch05-Sentence Embedding (LSA, Doc2Vec, LDA, ELMo).pdf ├── ch05-Transformer.pdf 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