Information Package / Course Catalogue
Statistical Natural Language Processing
Course Code: CSE440
Course Type: Area Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: English
Work Placement: N/A
Theory: 2
Prt.: 2
Credit: 3
Lab: 0
ECTS: 6
Objectives of the Course

Introducing the fundamentals of statistical natural language processing ,neural approaches, concepts, and applications. Learning current transformer architectures. Enabling students to statistical natural language processing projects with Python.

Course Content

The current technical approaches for linguistic problems, parsing, sentence meaning, word senses, semantic role labeling, coreference resolution, machine translation, chatbots.

Name of Lecturer(s)
Assoc. Prof. Fatih SOYGAZİ
Learning Outcomes
1.Understanding how core natural language processing tasks can be applied with statistical approach.
2.Understanding how core natural language processing tasks can be applied with neural approach.
3.Become more interested in developing new statisctical natural language processing for solving different types of problems
4.Ability to adapt statistical natural language processing to problems especially in Computer Engineering
5.Developing natural language processing projects for various tasks
Recommended or Required Reading
1.Dan Jurafsky and James H. Martin, “Speech and Language Processing”, 3rd Ed., Prentice Hall, 2023.
Weekly Detailed Course Contents
Week 1 - Theoretical & Practice
Introduction
Week 2 - Theoretical & Practice
Core Concepts
Week 3 - Theoretical & Practice
Context-Free Grammars and Constituency Parsing
Week 4 - Theoretical & Practice
Dependency Parsing
Week 5 - Theoretical & Practice
Relation and Event Extraction
Week 6 - Theoretical & Practice
Word Senses and Wordnet
Week 7 - Theoretical & Practice
Word Senses and Wordnet
Week 8 - Theoretical & Practice
Lexicons for Sentiment, Affect and Connotation
Week 9 - Theoretical & Practice
Lexicons for Sentiment, Affect and Connotation
Week 10 - Theoretical & Practice
Coreference Resolution
Week 11 - Theoretical & Practice
Transformers and Pretrained Language Models
Week 12 - Theoretical & Practice
Fine-tuning and Masked Language Models
Week 13 - Theoretical & Practice
Machine Translation
Week 14 - Theoretical & Practice
Question Answering Systems and Information Retrieval, Chatbots and Dialogue Systems
Assessment Methods and Criteria
Type of AssessmentCountPercent
Project2%100
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory141242
Lecture - Practice140228
Assignment140342
Project2101040
TOTAL WORKLOAD (hours)152
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
OÇ-1
3
3
3
3
4
4
4
4
2
3
3
OÇ-2
4
5
3
3
3
4
3
3
4
5
2
OÇ-3
5
5
5
5
4
3
3
3
3
3
4
OÇ-4
4
4
4
4
4
4
4
5
5
3
4
OÇ-5
4
4
4
3
5
5
5
2
3
3
4
Adnan Menderes University - Information Package / Course Catalogue
2026