The objective of this course is to explore recent research areas within natural language processing with sufficient depth. By the end of the course, participants will be equipped to actively contribute to research within their chosen subjects. This course is aimed for graduate students in computer science/engineering. The course assumes that students have a foundational knowledge of machine learning and prior experience or coursework in natural language processing. Topics covered encompass natural language understanding, representation learning, contextual representations, multitask learning, learning from multiple modalities, deep generative models, reinforcement learning, generative adversarial learning, NLP methods and metrics. The specific list of topics for the current year will be dependent on the instructor and prevailing trends in natural language processing research, with details announced during the course.
Course Content
This course contains; Natural language understanding,Representation learning,Contextual representation models,Semantic and syntactic parsing,Question answering,Machine translation,Exam week,Multitask learning, Learning from multiple modalities,Language generation,Deep generative models,Large language models,Reinforcement learning,Generative adversarial learning ,Project/research presentations.
Course Learning Outcomes
Teaching Methods
Assessment Methods
1 - Acquire knowledge about the selected advanced topics in natural language processing with a focus on design of learning algorithms and evaluation of learning algorithms
2
E
2 - Develop the ability to read and understand recent scientific literature in the field of natural language processing, apply the knowledge obtained by reading scientific papers, discuss and compare methods and assess their potentials and shortcomings
16, 2
D, F
3 - Gain a comprehensive understanding of advanced methods, and apply this knowledge to solutions of practical problems
12, 14, 21, 6, 9
A, D, G
4 - Carry out research projects in a chosen area of interest within natural language processing.
14, 2
F
Teaching Methods:
12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 2: Project Based Learning Model, 21: Simulation Technique, 6: Experiential Learning, 9: Lecture Method
Assessment Methods:
A: Traditional Written Exam, D: Oral Exam, E: Homework, F: Project Task, G: Quiz
Course Outline
Order
Subjects
Preliminary Work
1
Natural language understanding
2
Representation learning
3
Contextual representation models
4
Semantic and syntactic parsing
5
Question answering
6
Machine translation
7
Exam week
8
Multitask learning, Learning from multiple modalities
9
Language generation
10
Deep generative models
11
Large language models
12
Reinforcement learning
13
Generative adversarial learning
14
Project/research presentations
Resources
- Eisenstein (2019), Introduction to Natural Language Processing.
- Jurafsky and Martin (~2021), Speech and Language Processing.
- Manning and Schütze, Foundations of Statistical NLP.
- Murphy, Machine Learning: a Probabilistic Perspective
- Goodfellow, Bengio and Courville (2016), Deep Learning.
- Bird et al, NLP with Python, a.k.a. the NLTK book.
- Lewis Tunstall, Leandro von Werra, and Thomas Wolf. Natural Language Processing with Transformers
- Selected Papers.
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
Develop and deepen the current and advanced knowledge in the field with original thought and/or research and come up with innovative definitions based on Master's degree qualifications.
X
2
Conceive the interdisciplinary interaction which the field is related with ; come up with original solutions by using knowledge requiring proficiency on analysis, synthesis and assessment of new and complex ideas.
X
3
Evaluate and use new information within the field in a systematic approach and gain advanced level skills in the use of research methods in the field.
X
4
Develop an innovative knowledge, method, design and/or practice or adapt an already known knowledge, method, design and/or practice to another field.
5
Broaden the borders of the knowledge in the field by producing or interpreting an original work or publishing at least one scientific paper in the field in national and/or international refereed journals.
X
6
Contribute to the transition of the community to an information society and its sustainability process by introducing scientific, technological, social or cultural improvements.
7
Independently perceive, design, apply, finalize and conduct a novel research process.
X
8
Ability to communicate and discuss orally, in written and visually with peers by using a foreign language at least at a level of European Language Portfolio C1 General Level.
X
9
Critical analysis, synthesis and evaluation of new and complex ideas in the field.
X
10
Recognizes the scientific, technological, social or cultural improvements of the field and contribute to the solution finding process regarding social, scientific, cultural and ethical problems in the field and support the development of these values.
X
Assessment Methods
Contribution Level
Absolute Evaluation
Rate of Midterm Exam to Success
50
Rate of Final Exam to Success
50
Total
100
ECTS / Workload Table
Activities
Number of
Duration(Hour)
Total Workload(Hour)
Course Hours
14
3
42
Guided Problem Solving
0
0
0
Resolution of Homework Problems and Submission as a Report
10
2
20
Term Project
0
0
0
Presentation of Project / Seminar
8
10
80
Quiz
6
3
18
Midterm Exam
1
30
30
General Exam
1
50
50
Performance Task, Maintenance Plan
0
0
0
Total Workload(Hour)
240
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(240/30)
8
ECTS of the course: 30 hours of work is counted as 1 ECTS credit.
Detail Informations of the Course
Course Description
Course
Code
Semester
T+P (Hour)
Credit
ECTS
ADVANCED TOPICS in NATURAL LANGUAGE PROCESSING
COED1213993
Spring Semester
3+0
3
8
Course Program
Prerequisites Courses
Recommended Elective Courses
Language of Course
English
Course Level
Third Cycle (Doctorate Degree)
Course Type
Elective
Course Coordinator
Prof.Dr. Reda ALHAJJ
Name of Lecturer(s)
Prof.Dr. Reda ALHAJJ
Assistant(s)
Aim
The objective of this course is to explore recent research areas within natural language processing with sufficient depth. By the end of the course, participants will be equipped to actively contribute to research within their chosen subjects. This course is aimed for graduate students in computer science/engineering. The course assumes that students have a foundational knowledge of machine learning and prior experience or coursework in natural language processing. Topics covered encompass natural language understanding, representation learning, contextual representations, multitask learning, learning from multiple modalities, deep generative models, reinforcement learning, generative adversarial learning, NLP methods and metrics. The specific list of topics for the current year will be dependent on the instructor and prevailing trends in natural language processing research, with details announced during the course.
Course Content
This course contains; Natural language understanding,Representation learning,Contextual representation models,Semantic and syntactic parsing,Question answering,Machine translation,Exam week,Multitask learning, Learning from multiple modalities,Language generation,Deep generative models,Large language models,Reinforcement learning,Generative adversarial learning ,Project/research presentations.
Course Learning Outcomes
Teaching Methods
Assessment Methods
1 - Acquire knowledge about the selected advanced topics in natural language processing with a focus on design of learning algorithms and evaluation of learning algorithms
2
E
2 - Develop the ability to read and understand recent scientific literature in the field of natural language processing, apply the knowledge obtained by reading scientific papers, discuss and compare methods and assess their potentials and shortcomings
16, 2
D, F
3 - Gain a comprehensive understanding of advanced methods, and apply this knowledge to solutions of practical problems
12, 14, 21, 6, 9
A, D, G
4 - Carry out research projects in a chosen area of interest within natural language processing.
14, 2
F
Teaching Methods:
12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 2: Project Based Learning Model, 21: Simulation Technique, 6: Experiential Learning, 9: Lecture Method
Assessment Methods:
A: Traditional Written Exam, D: Oral Exam, E: Homework, F: Project Task, G: Quiz
Course Outline
Order
Subjects
Preliminary Work
1
Natural language understanding
2
Representation learning
3
Contextual representation models
4
Semantic and syntactic parsing
5
Question answering
6
Machine translation
7
Exam week
8
Multitask learning, Learning from multiple modalities
9
Language generation
10
Deep generative models
11
Large language models
12
Reinforcement learning
13
Generative adversarial learning
14
Project/research presentations
Resources
- Eisenstein (2019), Introduction to Natural Language Processing.
- Jurafsky and Martin (~2021), Speech and Language Processing.
- Manning and Schütze, Foundations of Statistical NLP.
- Murphy, Machine Learning: a Probabilistic Perspective
- Goodfellow, Bengio and Courville (2016), Deep Learning.
- Bird et al, NLP with Python, a.k.a. the NLTK book.
- Lewis Tunstall, Leandro von Werra, and Thomas Wolf. Natural Language Processing with Transformers
- Selected Papers.
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
Develop and deepen the current and advanced knowledge in the field with original thought and/or research and come up with innovative definitions based on Master's degree qualifications.
X
2
Conceive the interdisciplinary interaction which the field is related with ; come up with original solutions by using knowledge requiring proficiency on analysis, synthesis and assessment of new and complex ideas.
X
3
Evaluate and use new information within the field in a systematic approach and gain advanced level skills in the use of research methods in the field.
X
4
Develop an innovative knowledge, method, design and/or practice or adapt an already known knowledge, method, design and/or practice to another field.
5
Broaden the borders of the knowledge in the field by producing or interpreting an original work or publishing at least one scientific paper in the field in national and/or international refereed journals.
X
6
Contribute to the transition of the community to an information society and its sustainability process by introducing scientific, technological, social or cultural improvements.
7
Independently perceive, design, apply, finalize and conduct a novel research process.
X
8
Ability to communicate and discuss orally, in written and visually with peers by using a foreign language at least at a level of European Language Portfolio C1 General Level.
X
9
Critical analysis, synthesis and evaluation of new and complex ideas in the field.
X
10
Recognizes the scientific, technological, social or cultural improvements of the field and contribute to the solution finding process regarding social, scientific, cultural and ethical problems in the field and support the development of these values.
