Information Package / Course Catalogue
Natural Language Processing
Course Code: MTK569
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 8
Objectives of the Course

The purpose of this course is to introduce the modern methods of Natural Language Processing to students, and is to gain the ability to do research on the field of Natural Language Processing.

Course Content

Introduction to natural language processing and principles of computational linguistics, grammars and languages, language models, Part of Speech Tagging (POS), Statistical language models, corpus and n-gram Hidden Markov model, Viterbi algorithm, machine learning and some applications of Natural Language Processing.

Name of Lecturer(s)
Assoc. Prof. Korhan GÜNEL
Learning Outcomes
1.To be able to develop Natural Language Processing applications
2.To be able to recognize specific issues in natural language processing for Turkish
3.To be able to bring innovation to Natural Language Processing approaches
4.To be able to use concepts in solving certain types of problems
5.To be able to develop analytical skills and apply to problems
Recommended or Required Reading
1.D. Jurafsky and J. H. Martin, "Speech and Language Processing" , Prentice Hall, 2000.
2.Christopher D. Manning and Hinrich Schuetze, “ Foundations of Statistical Natural Language Processing”, 1999.
3.Alexander Clark, Chris Fox, and Shalom Lappin, "The Handbook of Computational Linguistics and Natural Language Processing", Wiley & Sons, 2010.
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to natural language processing
Week 2 - Theoretical
Principles of computational linguistics
Week 2 - Preparation Work
Relevant part of course book should be read
Week 3 - Theoretical
Grammars and languages
Week 3 - Preparation Work
Relevant part of course book should be read.
Week 4 - Theoretical
Language models
Week 4 - Preparation Work
Relevant part of course book should be read
Week 5 - Theoretical
Part of Speech Tagging (POS)
Week 5 - Preparation Work
Relevant part of course book should be read
Week 6 - Theoretical
Corpus and n-gram
Week 6 - Preparation Work
Relevant part of course book should be read
Week 7 - Theoretical
Statistical language models and detection of dictation errors
Week 7 - Preparation Work
Relevant part of course book should be read
Week 8 - Theoretical
Hidden Markov model, Viterbi algorithm
Week 8 - Preparation Work
Relevant part of course book should be read
Week 9 - Theoretical
Classification of text, Midterm Exam
Week 9 - Preparation Work
All subjects covered
Week 10 - Theoretical
Classification of text
Week 10 - Preparation Work
Relevant part of course book should be read
Week 11 - Theoretical
Information extraction
Week 11 - Preparation Work
Relevant part of course book should be read
Week 12 - Theoretical
Machine learning
Week 12 - Preparation Work
Relevant part of course book should be read
Week 13 - Theoretical
Machine learning
Week 13 - Preparation Work
Relevant part of course book should be read
Week 14 - Theoretical
Machine learning
Week 14 - Preparation Work
Relevant part of course book should be read
Week 15 - Theoretical
FINAL EXAM
Week 15 - Preparation Work
Relevant part of course book should be read
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%30
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140342
Individual Work140456
Midterm Examination142345
Final Examination154357
TOTAL WORKLOAD (hours)200
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
PÇ-12
PÇ-13
PÇ-14
PÇ-15
OÇ-1
3
3
3
4
3
5
5
OÇ-2
3
3
3
4
4
OÇ-3
4
4
5
5
3
4
4
4
3
3
3
4
OÇ-4
4
4
5
5
3
4
4
4
3
3
3
4
OÇ-5
4
4
5
5
3
4
4
4
3
3
3
4
Adnan Menderes University - Information Package / Course Catalogue
2026