Natural Language Processing
Undergraduate Course, CuCEng, 2025
Course Objectives
In this course, the main goal is to define the methods and approaches used in Natural Language Processing.
Course Materials
- Daniel Jurafsky and James H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech, 2000.
Assessment
40% Midterm (exam,tasks,etc.) + 60% Final (exam,tasks,etc.)
Prerequisites
There is no formal prerequisite; however, taking the Theory of Computation (Automata Theory) course beforehand is recommended.
Weekly Schedule
| Week | Subjects | Note |
|---|---|---|
| 1 | Introduction to NLP: Concepts and terms | Lesson 1 |
| 2 | Text Normalization, Lemmatization, Parsing | Lesson 2 |
| 3 | N-Grams and Language Models | Lesson 3 |
| 4 | Corpus (Features and Analysis) | Lesson 4 |
| 5 | Part of Speech Tagging | Lesson 5 |
| 6 | Introduction to Semantic Analysis | Lesson 6 |
| 7 | Ambiguity | Lesson 7 |
| 8 | Midterm Exam | |
| 9 | Lexical Similarity | Lesson 8 |
| 10 | Semantic Similarity | Lesson 9 |
| 11 | Dialogue Systems, Question Answering | Lesson 10 |
| 12 | Machine Translation | Lesson 11 |
| 13 | Keyword Extraction, Document Summarization | Lesson 12 |
| 14 | Paraphrasing, Ontology Mapping | Lesson 13 |
| 15 | Project presentations | RAG - LoRA - Agent |
Resources
Below you can find past exam papers.
nlp2017f-e.pdf | nlp2017m-e.pdf | nlp2018f-e.pdf | nlp2018m-e.pdf | nlp2019f-e.pdf | nlp2019m-e.pdf