Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|
| NATURAL LANGUAGE UNDERSTANDING | COED1114312 | Fall Semester | 3+0 | 3 | 8 |
| Prerequisites Courses | |
| Recommended Elective Courses | |
| Language of Course | English |
| Course Level | Third Cycle (Doctorate Degree) |
| Course Type | Elective |
| Course Coordinator | Prof.Dr. Selim AKYOKUŞ |
| Name of Lecturer(s) | Prof.Dr. Selim AKYOKUŞ |
| Assistant(s) | |
| Aim | The objective of this course is to teach theoretical concepts, methods and algorithms from linguistics, natural language processing (NLU), and machine learning methods to develop systems and algorithms to understand natural languages efficiently and reliably. Topics include lexical semantics, distributed representations of meaning, contextual language representation, large language models, information retrieval, and advanced evaluations of NLU models, relation extraction, semantic parsing, sentiment analysis, and dialogue agents. Students are expected to develop a project in natural language understanding with a focus on following best practices in the field. |
| Course Content | This course contains; Introduction to Natural Language Understanding (NLU),Matrix designs for representations of text, weighting methods, distances, and vector comparisons.,Dimensionality reduction and representation learning.,Distributed word representations,Supervised sentiment analysis,Deep Learning and Transformers,Contextual Representation Models,Exam Week,Large Language Models,Fine-tuning large language models,NLU and Information Retrieval (IR) ,Grounded language understanding,Model evaluation methods and metrics
,Project presentations. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 2. Comprehend semantic and syntactic relationships among words using contextual word representation models like transformers, BERT, ELECTRA, and GPT. | 16, 2 | D, F |
| 3. Construct neural information retrieval systems and retrieve specific information from texts employing both classical and neural information retrieval techniques. | 12, 14, 21, 6, 9 | A, D, G |
| 4. Design/implement a Natural Language Understanding (NLU) research project based on your preferences. | 14, 2 | F |
| 1. Develop robust language models, machine learning systems and algorithms to understand human language effectively. | 2 | E |
| 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 | Introduction to Natural Language Understanding (NLU) | |
| 2 | Matrix designs for representations of text, weighting methods, distances, and vector comparisons. | |
| 3 | Dimensionality reduction and representation learning. | |
| 4 | Distributed word representations | |
| 5 | Supervised sentiment analysis | |
| 6 | Deep Learning and Transformers | |
| 7 | Contextual Representation Models | |
| 8 | Exam Week | |
| 9 | Large Language Models | |
| 10 | Fine-tuning large language models | |
| 11 | NLU and Information Retrieval (IR) | |
| 12 | Grounded language understanding | |
| 13 | Model evaluation methods and metrics
| |
| 14 | Project presentations | |
| Resources |
| - Dan Jurafsky and James H. Martin. Speech and Language Processing
- Jacob Eisenstein. Natural Language Processing
- Yoav Goldberg. A Primer on Neural Network Models for Natural Language Processing
- Delip Rao and Brian McMahan. Natural Language Processing with PyTorch
- Lewis Tunstall, Leandro von Werra, and Thomas Wolf. Natural Language Processing with Transformers
- Selected Papers
- State of art software resources in NLU
|
| Natural Language Processing with Python, Steven Bird, Ewan Klein, and Edward Loper at http://www.nltk.org/book/ |
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 |
|---|
| NATURAL LANGUAGE UNDERSTANDING | COED1114312 | Fall Semester | 3+0 | 3 | 8 |
| Prerequisites Courses | |
| Recommended Elective Courses | |
| Language of Course | English |
| Course Level | Third Cycle (Doctorate Degree) |
| Course Type | Elective |
| Course Coordinator | Prof.Dr. Selim AKYOKUŞ |
| Name of Lecturer(s) | Prof.Dr. Selim AKYOKUŞ |
| Assistant(s) | |
| Aim | The objective of this course is to teach theoretical concepts, methods and algorithms from linguistics, natural language processing (NLU), and machine learning methods to develop systems and algorithms to understand natural languages efficiently and reliably. Topics include lexical semantics, distributed representations of meaning, contextual language representation, large language models, information retrieval, and advanced evaluations of NLU models, relation extraction, semantic parsing, sentiment analysis, and dialogue agents. Students are expected to develop a project in natural language understanding with a focus on following best practices in the field. |
| Course Content | This course contains; Introduction to Natural Language Understanding (NLU),Matrix designs for representations of text, weighting methods, distances, and vector comparisons.,Dimensionality reduction and representation learning.,Distributed word representations,Supervised sentiment analysis,Deep Learning and Transformers,Contextual Representation Models,Exam Week,Large Language Models,Fine-tuning large language models,NLU and Information Retrieval (IR) ,Grounded language understanding,Model evaluation methods and metrics
,Project presentations. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 2. Comprehend semantic and syntactic relationships among words using contextual word representation models like transformers, BERT, ELECTRA, and GPT. | 16, 2 | D, F |
| 3. Construct neural information retrieval systems and retrieve specific information from texts employing both classical and neural information retrieval techniques. | 12, 14, 21, 6, 9 | A, D, G |
| 4. Design/implement a Natural Language Understanding (NLU) research project based on your preferences. | 14, 2 | F |
| 1. Develop robust language models, machine learning systems and algorithms to understand human language effectively. | 2 | E |
| 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 | Introduction to Natural Language Understanding (NLU) | |
| 2 | Matrix designs for representations of text, weighting methods, distances, and vector comparisons. | |
| 3 | Dimensionality reduction and representation learning. | |
| 4 | Distributed word representations | |
| 5 | Supervised sentiment analysis | |
| 6 | Deep Learning and Transformers | |
| 7 | Contextual Representation Models | |
| 8 | Exam Week | |
| 9 | Large Language Models | |
| 10 | Fine-tuning large language models | |
| 11 | NLU and Information Retrieval (IR) | |
| 12 | Grounded language understanding | |
| 13 | Model evaluation methods and metrics
| |
| 14 | Project presentations | |
| Resources |
| - Dan Jurafsky and James H. Martin. Speech and Language Processing
- Jacob Eisenstein. Natural Language Processing
- Yoav Goldberg. A Primer on Neural Network Models for Natural Language Processing
- Delip Rao and Brian McMahan. Natural Language Processing with PyTorch
- Lewis Tunstall, Leandro von Werra, and Thomas Wolf. Natural Language Processing with Transformers
- Selected Papers
- State of art software resources in NLU
|
| Natural Language Processing with Python, Steven Bird, Ewan Klein, and Edward Loper at http://www.nltk.org/book/ |
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 |
Numerical Data
Ekleme Tarihi: 24/12/2023 - 02:34Son Güncelleme Tarihi: 24/12/2023 - 02:34
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