This repository serves as a comprehensive collection of lecture materials, slides, and reading resources for a graduate-level Text Analytics course at Korea University. It covers a wide range of Natural Language Processing (NLP) topics, from foundational concepts to advanced deep learning models, making it a valuable resource for students and researchers in the field.

How It Works

The repository is structured around a detailed course syllabus, providing a chronological breakdown of topics covered throughout a semester. Each topic includes links to lecture slides, video recordings, and relevant academic papers. The content progresses from basic text preprocessing and classic representation methods (like Bag-of-Words) to modern deep learning approaches, including Word2Vec, GloVe, RNNs, CNNs, and state-of-the-art pre-trained models like BERT and GPT.

Quick Start & Requirements

This repository is primarily a collection of educational materials and does not have a direct "quick start" for running code. However, the content itself requires a strong understanding of NLP concepts and familiarity with machine learning frameworks. The reading materials often link to PDF versions of research papers.

Highlighted Details

• Extensive coverage of foundational and advanced NLP techniques, including classic methods and deep learning architectures.
• Detailed exploration of text representation methods, from n-grams to distributed embeddings like Word2Vec and GloVe.
• In-depth treatment of modern language models, including Seq2Seq, Transformers, ELMo, GPT, and BERT.
• Practical applications such as document classification, sentiment analysis, and text summarization are discussed with relevant research papers.
• Links to external resources like Stanford's CS224d and CS224n courses provide supplementary learning opportunities.

Maintenance & Community

The repository appears to be associated with a specific academic course (2021 Spring) and its associated term projects. There is no indication of ongoing community development or active maintenance beyond the scope of that academic offering.

Licensing & Compatibility

The licensing information is not explicitly stated in the README. Given the academic nature and the inclusion of links to various research papers, users should assume that the content is for educational and non-commercial use unless otherwise specified by the linked sources.

Limitations & Caveats

This repository is a static collection of course materials from a specific academic term (2021 Spring) and does not contain executable code or a framework for direct use. The content is focused on theoretical understanding and research paper summaries rather than practical implementation.