| Course Type | Elective Course |
|---|
| Course Level | Master's Degree Without Thesis |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 10 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- Understand the foundational principles of computational linguistics.
- Acquires the foundational methods used in computational linguistics.
- Develops language processing applications and gains hands-on experience.
- Knows how to apply the learned methods to a specific linguistic problem.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | No other optional course are recommended. |
|---|
| Course Description | Introduction to Computational Linguistics, Language Models and N-grams, Named Entity Recognition and Relation Extraction, Text Classification and Methods, Markov Models and Part-of-Speech Tagging, Context-Free Grammar and Parsing, Parsing Methods, Semantics and Lambda Calculus, Lexical Semantics and Word Sense Disambiguation, Information Retrieval, Machine Translation, Recent Research and Applications |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to Computational Linguistics |
|
2nd Week |
Language Models and N-grams |
|
3rd Week |
Named Entity Recognition and Relation Extraction |
|
4th Week |
Text Classification and Methods |
|
5th Week |
Markov Models and Part-of-Speech Tagging |
|
6th Week |
Context-Free Grammar and Parsing |
|
7th Week |
Parsing Methods |
|
8th Week |
Midterm |
|
9th Week |
Semantics and Lambda Calculus |
|
10th Week |
Lexical Semantics and Word Sense Disambiguation |
|
11th Week |
Information Retrieval |
|
12th Week |
Machine Translation |
|
13th Week |
Recent Research and Applications |
|
14th Week |
Recent Research and Applications |
|
| Recommended or Required Reading | 1. Speech and Language Processing, Jurafsky, D. & Martin, J. Prentice Hall, 2008.
2. Foundations of Statistical Natural Language Processing, Christopher D. Manning, and Hinrich Schutze. The MIT Press, 1999. |
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Questions/Answers
- Project
- Presentation
- Report Preparation
- Practice
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Quiz |
1 |
15 |
| Project |
1 |
15 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
2 |
2 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
11 |
154 |
| Preparation for Final exam |
1 |
69 |
69 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
44 |
44 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
|
|
|
| Total Work Load | 313 |
| Total Work Load / 30 (h) | 10.43 |
| ECTS Credit of the Course | 10 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
