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All Projects → pilsung-kang → Text Analytics

pilsung-kang / Text Analytics

Unstructured Data Analysis (Graduate) @Korea University

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text-analytics

Unstructured Data Analysis (Graduate) @Korea University

Notice

  • Syllabus (download)
  • Term Project Presentations and Youtube summary (2021 Spring) (link)
    • Term Project Presentations and Youtube summary (2020 Spring) (link)

Schedule

  • 2021-03-09 (Tue): Term Project Meeting (Round 1)
    • 1조: 강형원, 김지나, 김탁영, 김수빈
    • 2조: 소규성, 이윤승, 정의석
    • 3조: 김정섭, 허재혁, 최정우, 윤훈상
    • 4조: 정은영, 오혜성 조민영
  • 2021-03-11 (Thu): Term Project Meeting (Round 1)
    • 5조: 김동균, 이찬호, 차형주
    • 6조: 김형주, 천주영, 신동환
    • 7조: 허종국, 임새린, 고은지, 황석철
    • 8조: 김아름, 최병록, 임민아, 임수연
  • 2021-03-16 (Tue): Topic Discussion and QA (Topic 1, 2, 4)
    • 동영상 시청 완료 및 질문 등록: 2021-03-09
    • 질문에 대한 수강생 답변 등록: 2021-03-14
    • 유튜브 요약 영상 업로드: 2021-03-18
  • 2021-03-18 (Thu): No class
  • 2021-03-22 (Tue): Term Project Meeting (Round 1)
    • 9조: 유이경, 고은성, 조경선, 김지은
    • 10조: 김태연, 안시후, 오혜령, 조한샘
    • 11조: 안인범, 김정원, 황성진, 김상민
    • 12조: 정회찬, 신우석, 양석우, 노상균, 김진섭, 김영섭
  • 2021-03-25 (Thu): No class
  • 2021-03-30 (Tue): Topic Discussion and QA (Topic 5)
    • 동영상 시청 완료 및 질문 등록: 2021-03-23
    • 질문에 대한 수강생 답변 등록: 2021-03-28
    • 유튜브 요약 영상 업로드: 2021-04-01
  • 2021-04-01 (Thu): Term Project Meeting (Round 2)
    • 1조, 2조, 3조, 4조
  • 2021-04-06 (Tue): Topic Discussion and QA (Topic 6)
    • 동영상 시청 완료 및 질문 등록: 2021-03-30
    • 질문에 대한 수강생 답변 등록: 2021-04-04
    • 유튜브 요약 영상 업로드: 2021-04-08
  • 2021-04-08 (Thu): Term Project Meeting (Round 2)
    • 5조, 6조, 7조, 8조
  • 2021-04-13 (Tue): Topic Discussion and QA (Topic 7)
    • 동영상 시청 완료 및 질문 등록: 2021-04-06
    • 질문에 대한 수강생 답변 등록: 2021-04-11
    • 유튜브 요약 영상 업로드: 2021-04-15
  • 2021-04-15 (Thu): Term Project Meeting (Round 2)
    • 9조, 10조, 11조, 12조

Recommended courses

Contents

Topic 1: Introduction to Text Analytics

  • Text Analytics: Backgrounds, Applications, & Challanges, and Process [Slide], [Video] (2021-03-09)
  • Text Analytics Process [Slide], [Video] (2021-03-09)

Topic 2: Text Preprocessing

  • Introduction to Natural Language Processing (NLP) [Slide], [Video] (2021-03-09)
  • Lexical analysis [Slide], [Video] (2021-03-09)
  • Syntax analysis & Other topics in NLP [Slide], [Video] (2021-03-09)
  • 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)
    • NLP Year in Review - 2019 (Medium Post)

Topic 3: Neural Networks Basics (Optional, No Video Lectures)

  • Perception, Multi-layered Perceptron
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Practical Techniques

Topic 4: Text Representation I: Classic Methods

  • Bag of words, Word weighting, N-grams [Slide], [Video] (2021-03-09)

Topic 5: Text Representation II: Distributed Representation

  • Neural Network Language Model (NNLM) [Slide], [Video] (2021-03-23)
  • Word2Vec [Slide], [Video], [Optional Video (발표자: 김지나)] (2021-03-23)
  • GloVe [Slide], [Video], [Optional Video (발표자 조규원)] (2021-03-23)
  • FastText, Doc2Vec, and Other Embeddings [Slide], [Video] (2021-03-23)
  • (Optional) Analogies Explained: Towards Understanding Word Embeddings [Video (발표자: 김명섭)]
  • Reading materials
    • Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A neural probabilistic language model. Journal of machine learning research, 3(Feb), 1137-1155. (PDF)
    • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781. (PDF)
    • Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111-3119). (PDF)
    • Pennington, J., Socher, R., & Manning, C. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543). (PDF)
    • Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2016). Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606. (PDF)

Topic 6: Dimensionality Reduction

  • Dimensionality Reduction Overview, Supervised Feature Selection [Slide], [Video] (2021-03-30)
  • Unsupervised Feature Extraction [Slide], [Video] (2021-03-30)
  • Reading materials
    • Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American society for information science, 41(6), 391-407. (PDF)
    • Landauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse processes, 25(2-3), 259-284. (PDF)
    • Maaten, L. V. D., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of machine learning research, 9(Nov), 2579-2605. (PDF) (Homepage)

Topic 7: Topic Modeling as a Distributed Reprentation

  • Topic modeling overview & Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis: pLSA [Slide], [Video] (2021-04-06)
  • LDA: Document Generation Process [Slide], [Video] (2021-04-06)
  • LDA Inference: Collapsed Gibbs Sampling, LDA Evaluation [Slide], [Video] (2021-04-06)
  • Reading Materials
    • Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American society for information science, 41(6), 391. (PDF)
    • Dumais, S. T. (2004). Latent semantic analysis. Annual review of information science and technology, 38(1), 188-230.
    • Hofmann, T. (1999, July). Probabilistic latent semantic analysis. In Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence (pp. 289-296). Morgan Kaufmann Publishers Inc. (PDF)
    • Hofmann, T. (2017, August). Probabilistic latent semantic indexing. In ACM SIGIR Forum (Vol. 51, No. 2, pp. 211-218). ACM.
    • Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77-84. (PDF)
    • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022. (PDF) [Optional Video (발표자: 윤훈상)]
  • Recommended video lectures

Topic 8: Language Modeling & Pre-trained Models

  • Sequence-to-Sequence Learning [Slide], [Video]
  • Transformer [Slide], [Video], [Optional Video (발표자: 김동화)]
  • ELMo: Embeddings from Language Models [Slide], [Video]
  • GPT: Generative Pre-Training of a Language Model [Slide], [Video]
  • BERT: Bidirectional Encoder Representations from Transformer [Slide], [Video]
  • GPT-2: Language Models are Unsupervised Multitask Learners [Slide], [Video]
  • Transformer to T5 (XLNet, RoBERTa, MASS, BART, MT-DNN,T5) [Video (발표자: 이유경)]
  • GPT-3: Language Models are Few Shot Learners [Slide], [Video]
  • (Optional) How Contextual are Contextualized Word Representations? Comparing the Geoetry of BERT, ELMo, and GPT-2 Embeddings [Slide], [Video (발표자: 이유경)]
  • (Optional) Syntax and Semantics in Language Model Representation [Video (발표자: 김명섭)]
  • Reading Materials
  • Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. In Advances in neural information processing systems (pp. 3104-3112). (PDF)
    • Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473. (PDF)
    • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008). (PDF)
    • Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. arXiv preprint arXiv:1802.05365. (PDF)
    • Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. (PDF)
    • Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. (PDF)
    • Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9. (PDF)
    • Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019). XLNet: Generalized autoregressive pretraining for language understanding. arXiv preprint arXiv:1906.08237. (PDF)
    • Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., ... & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. (PDF)
    • Song, K., Tan, X., Qin, T., Lu, J., & Liu, T. Y. (2019). Mass: Masked sequence to sequence pre-training for language generation. arXiv preprint arXiv:1905.02450. (PDF)
    • Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., ... & Zettlemoyer, L. (2019). Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461. (PDF)
    • Liu, X., He, P., Chen, W., & Gao, J. (2019). Multi-task deep neural networks for natural language understanding. arXiv preprint arXiv:1901.11504. (PDF)
    • Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2019). Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683. (PDF)

Topic 9: Document Classification

  • Document classification overview, Vector Space Models (Naive Bayesian Classifier, k-Nearese Neighbor Classifier) [Slide], [Video]
  • (Optional) Other VSM-based classsification (Lecture videos are taken from IMEN415 (Multivariate Data Analysis for Undergraudate Students @Korea University))
  • CNN-based document classification [Slide], [Video], [(Optional) 발표자 이기창]
  • RNN-based document classification [Slide], [Video]
  • Reading materials
    • Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882. (PDF)
    • Zhang, X., Zhao, J., & LeCun, Y. (2015). Character-level convolutional networks for text classification. In Advances in neural information processing systems (pp. 649-657) (PDF)
    • Lee, G., Jeong, J., Seo, S., Kim, C, & Kang, P. (2018). Sentiment classification with word localization based on weakly supervised learning with a convolutional neural network. Knowledge-Based Systems, 152, 70-82. (PDF)
    • Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016). Hierarchical attention networks for document classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 1480-1489). (PDF)
    • Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473. (PDF)
    • Luong, M. T., Pham, H., & Manning, C. D. (2015). Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025. (PDF)

Topic 10: Sentiment Analysis

  • Architecture of sentiment analysis [Slide], [Video]
  • Lexicon-based approach [Slide], [Video]
  • Machine learning-based approach [Slide], [Video]
  • Reading materials
    • Hamilton, W. L., Clark, K., Leskovec, J., & Jurafsky, D. (2016, November). Inducing domain-specific sentiment lexicons from unlabeled corpora. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing (Vol. 2016, p. 595). NIH Public Access. (PDF)
    • Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253. (PDF)

Topic 11: Document Summarization

Topic 12: Question and Answering

Topic 13: (Open) Information Extraction

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