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Teaching
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Natural Language Processing
1. Introduction and Dialogue System
2. Dialogue Systems
3. Text Preprocessing (Regular Expressions and Automata)
4. Text Tokenization & Edit Distance
5. Hidden Markov Models
6. N-gram Language Models
6-1. N-gram Smoothing
7. Naive Bayes andClassification
8. Logistic Regression and Conditional Random Fields
9. Vector Semantics and Embedding
(Google Colab) TF-IDF + Naive Bayes Classifier for Sarcastic Detection.ipynb
(Google Colab) Word2Vec Logistic Regression for Sarcastic Detection.ipynb
Midterm Review
HW1 Review
10. Neural Network & Neural Language Model
11. Transformer (+Mixture of Expert)
12. POS Tagging
HW2
13. Context Free Grammars (Constituency Parsing) and Dependency Parsing
14. Retrieval Augmented Generation (RAG), Introduction and Retriever
15. Retrieval Augmented Generation (RAG), Generator, and Recent Works