Unstructured Data Analysis (Graduate) @Korea University

• Term Project Group

  • 1조: 손규빈 박경찬 최희정
  • 2조: 이재융 김혜민 이수연
  • 3조: 이주현 권원진 정하은 최현석
  • 4조: 음수민 유지원 김웅 전은석 권구포
  • 5조: 이도명 김강민 이중호 김다애
  • 6조: 채선율 성유연 이창현
  • 7조: 안지영 송재승 김문수 최지은
  • 8조: 최현율 황정임 조억 김명소
  • 9조: 이민형 이선화 손주희 김준호
  • 10조: 김다연 윤석채 우현희 안건이 이지윤
  • 11조: 송서하 양우식 정민성
  • 12조: 김동원 박재용 정연재

• Term project proposal evaluation (link)

• Term project proposal feedback (download)

• Recommended courses

  • CS224d @Stanford: Deep Learning for Natural Language Processing
    • Course Homepage: http://cs224d.stanford.edu/
    • YouTube Video: https://www.youtube.com/playlist?list=PLlJy-eBtNFt4CSVWYqscHDdP58M3zFHIG
  • CS224n @Stanford: Natural Language Processing Deep Learning
    • Course Homepage: http://web.stanford.edu/class/cs224n/syllabus.html
    • Youtube Video: https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6
  • Deep Natural Lanugage Processing @Oxford
    • Course Homepage: https://github.com/oxford-cs-deepnlp-2017/lectures

• The usefullness of large amount of text data and the challenges
• Overview of text mining methods

• Obtain texts to analyze
• Text data collection through APIs and web scraping

• Introduction to NLP
• Lexical analysis
• Syntax analysis
• Other topics in NLP
• Reading materials
  • Cambria, E., & White, B. (2014). Jumping NLP curves: A review of natural language processing research. IEEE Computational intelligence magazine, 9(2), 48-57. (PDF)
  • Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12(Aug), 2493-2537. (PDF)
  • Young, T., Hazarika, D., Poria, S., & Cambria, E. (2017). Recent trends in deep learning based natural language processing. arXiv preprint arXiv:1708.02709. (PDF)

• Bag of words
• Word weighting
• N-grams

• Word2Vec
• GloVe
• FastText
• Doc2Vec

• Dimensionality Reduction
• Supervised Feature Selection
• Unsupervised Feature Extraction: Latent Semantic Analysis (LSA) and t-SNE
• R Example

• Document similarity metrics
• Clustering overview
• K-Means clustering
• Hierarchical clustering
• Density-based clustering

• Document classification overview
• Naive Bayesian classifier
• k-Nearest Neighbor classifier
• Classification tree
• Support Vector Machine (SVM)

• Introduction to Neural Network
• Recurrent neural network-based document classification
• Convolutional neural network-based document classification

• Topic modeling overview
• Probabilistic Latent Semantic Analysis: pLSA
• LDA: Document Generation Process

• LDA Inference: Gibbs Sampling
• LDA Evaluation