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| BILL7141 | @Natural Language Processing | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of COMPUTER ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Elif BAYKAL KABLAN | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The main objectives of this course are as follows:
Understand the fundamental concepts and principles of natural language processing.
Understand and apply natural language processing techniques such as text mining, language modeling, and sentiment analysis.
Learn how machine learning and statistical methods are used in natural language processing applications.
Effectively use natural language processing tools and libraries.
Transform theoretical knowledge into practice by working on real-world natural language processing projects.
This course aims to provide students with a strong foundation in the field of natural language processing and to equip them with practical skills in this area. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | They will understand the fundamental concepts, principles, and applications of natural language processing. | 6,8 | 1, | | PO - 2 : | Their ability to apply natural language processing techniques such as text mining, language modeling, and sentiment analysis will be developed. | | 1,5, | | PO - 3 : | They will learn how to use machine learning and statistical methods in natural language processing applications. | | 1,5, | | PO - 4 : | Students will be equipped with the skills to effectively use natural language processing tools and libraries. | | 1,5, | | PO - 5 : | Their ability to apply natural language processing techniques such as text mining, language modeling, and sentiment analysis will be developed. | | 1,5, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Introduction to NLP, Basic Text Processing, Edit Distance, N-Grams, Naive Bayes for Sentiment Analysis, Logistic Regression, Vector Semantics and Embeddings, Sequence Labeling for POS Tagging |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Overview of NLP | | | Week 2 | Basic Text Processing
Regular Expressions
Application (Code in Python)
| | | Week 3 | Minimum Edit Distance
| | | Week 4 | Introduction to N-grams | | | Week 5 | The Task of Text Classification, Naive Bayes, Sentiment Classification | | | Week 6 | The Task of Text Classification, Naive Bayes, Sentiment Classification | | | Week 7 | Application of Sentiment Classification Example Python Code (Naive Bayes) | | | Week 8 | Background: Generative and Discriminative Classifiers, Logistic Regression | | | Week 9 | Midterm | | | Week 10 | Vector Semantics and Embeddings | | | Week 11 | Neural Networks (Sentiment Classification) | | | Week 12 | Sequence Labeling for POS Tagging | | | Week 13 | Student Presentations Part1 | | | Week 14 | Student Presentations Part2 | | | Week 15 | Student Presentations Part3 | | | Week 16 | Final Exam | | | |
| 1 | Jurafsky Daniel, Martin James H., Speech and Language Processin, Third Edition, Prentice Hall, 2018 | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | - | 2 | 30 | | Project | 14 | - | 12 | 20 | | End-of-term exam | 16 | - | 2 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 2 | 14 | 28 | | Arasınav için hazırlık | 2 | 5 | 10 | | Proje | 1 | 14 | 14 | | Dönem sonu sınavı için hazırlık | 2 | 5 | 10 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 106 |
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