Course Detail
Course Description
Course | Code | Semester | T+P (Hour) | Credit | ECTS |
---|
INTRODUCTION to NATURAL LANGUAGE PROCESSING | COE4212804 | Spring Semester | 3+0 | 3 | 6 |
Prerequisites Courses | |
Recommended Elective Courses | |
Language of Course | English |
Course Level | First Cycle (Bachelor's Degree) |
Course Type | Elective |
Course Coordinator | Prof.Dr. Selim AKYOKUŞ |
Name of Lecturer(s) | Prof.Dr. Selim AKYOKUŞ |
Assistant(s) | |
Aim | Natural language processing (NLP) is a crucial technology in the era of information age. Exciting advancements in natural language processing (NLP) have recently emerged, enabling systems that can perform tasks such as text translation, question answering, and spoken conversations. This course aims to provide students with a foundational understanding of NLP, including standard frameworks, algorithms, and techniques used to solve various NLP problems. The curriculum will cover topics like language modeling, representation learning, text classification, sequence tagging, syntactic parsing, machine translation, and question answering, with a particular focus on recent deep learning approaches. Through this course, students will receive a comprehensive introduction to NLP concepts, methods, algorithms, applications and state-of-the-art methods research in deep learning for NLP. |
Course Content | This course contains; Introduction to Natural Language Processing (NLP),Lingustic Essentials, Regular Exp., Text Normalization, Edit Distance,N-gram Models,Machine Learning Basics, Text Classification, Naive Bayes and Logistic Regression,Vector Semantics and Dense Word Embeddings,Neural Networks and Neural Language Models,Sequence Labeling for Parts of Speech and Named Entities,Exam Week,RNNs and LSTMs,Transformers and Pretrained Language Models, Fine Tuning and Masked Language Models,Machine Translation, Question Answering and Information Retrieval,Chatbots and Dialogue Systems, Automatic Speech Recognition and Text-to-Speech,Context-Free Grammars, Constituency Parsing, Dependency Parsing, Logical Representations of Sentence Meaning,Review and Project Presentations. |
Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
Decompose a real-world problem into subproblems in NLP, use existing natural language processing tools to conduct basic NLP, and identify potential solutions. | 11 | A, F |
Learn about the main uses of machine learning techniques and deep learning models in NLP. | | A, F, G |
Explain state-of-the-art methods to tackle NLP sub-problems, such as text representation, representation learning techniques, text mining, language modeling, and similarity detection, and gain a an understanding about the methods and metrics for various natural language processing tasks and applications. | | A, F, G |
Extract information from text automatically using concepts and methods from natural language processing (NLP) including stemming, n-grams, POS tagging, and parsing. | | A, E, F |
Get familiarized with the terminology, a breadth of concepts and tasks in NLP. | | A, G |
Teaching Methods: | 11: Demonstration Method |
Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task, G: Quiz |
Course Outline
Order | Subjects | Preliminary Work |
---|
1 | Introduction to Natural Language Processing (NLP) | Ch 1 |
2 | Lingustic Essentials, Regular Exp., Text Normalization, Edit Distance | Ch 2 |
3 | N-gram Models | Ch 3 |
4 | Machine Learning Basics, Text Classification, Naive Bayes and Logistic Regression | Ch 4, 5 |
5 | Vector Semantics and Dense Word Embeddings | Ch 6 |
6 | Neural Networks and Neural Language Models | Ch 7 |
7 | Sequence Labeling for Parts of Speech and Named Entities | Ch 8 |
8 | Exam Week | Ch 1-8 |
9 | RNNs and LSTMs | Ch 9 |
10 | Transformers and Pretrained Language Models, Fine Tuning and Masked Language Models | Ch 10, 11 |
11 | Machine Translation, Question Answering and Information Retrieval | Ch 13, 14 |
12 | Chatbots and Dialogue Systems, Automatic Speech Recognition and Text-to-Speech | Sohbet Robotları ve Diyalog Sistemleri, Otomatik Konuşma Tanıma ve Metinden Konuşmaya |
13 | Context-Free Grammars, Constituency Parsing, Dependency Parsing, Logical Representations of Sentence Meaning | Ch 17, 18, 19 |
14 | Review and Project Presentations | |
Resources |
- Speech and Language Processing, D.Jurafsky, J.H.Martin, 3rd Edition, Pearson-Prentice Hall.
- Foundations of Statistical Natural Language Processing, C.D.Manning, H.Schütze, MIT Press, 2002.
- Jacob Eisenstein, Introduction to Natural Language Processing, 2019. |
- Yoav Goldberg. A Primer on Neural Network Models for Natural Language Processing
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning
- Delip Rao and Brian McMahan. Natural Language Processing with PyTorch
- Lewis Tunstall, Leandro von Werra, and Thomas Wolf. Natural Language Processing with Transformers |
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications |
No | Program Qualification | Contribution Level |
1 | 2 | 3 | 4 | 5 |
1 | 1. An ability to apply knowledge of mathematics, science, and engineering | | | X | | |
2 | 2. An ability to identify, formulate, and solve engineering problems | | | | | X |
3 | 3. An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability | | | | X | |
4 | 4. An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice | | | | | X |
5 | 5. An ability to design and conduct experiments, as well as to analyze and interpret data | | | | | X |
6 | 6. An ability to function on multidisciplinary teams | X | | | | |
7 | 7. An ability to communicate effectively | | | | | X |
8 | 8. A recognition of the need for, and an ability to engage in life-long learning | | | X | | |
9 | 9. An understanding of professional and ethical responsibility | | | | X | |
10 | 10. A knowledge of contemporary issues | | | | | |
11 | 11. The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context | | | | X | |
Assessment Methods
Contribution Level | Absolute Evaluation |
Rate of Midterm Exam to Success | | 30 |
Rate of Final Exam to Success | | 70 |
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 | 6 | 12 | 72 |
Term Project | 0 | 0 | 0 |
Presentation of Project / Seminar | 2 | 8 | 16 |
Quiz | 0 | 0 | 0 |
Midterm Exam | 2 | 10 | 20 |
General Exam | 3 | 10 | 30 |
Performance Task, Maintenance Plan | 0 | 0 | 0 |
Total Workload(Hour) | 180 |
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(180/30) | 6 |
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 |
---|
INTRODUCTION to NATURAL LANGUAGE PROCESSING | COE4212804 | Spring Semester | 3+0 | 3 | 6 |
Prerequisites Courses | |
Recommended Elective Courses | |
Language of Course | English |
Course Level | First Cycle (Bachelor's Degree) |
Course Type | Elective |
Course Coordinator | Prof.Dr. Selim AKYOKUŞ |
Name of Lecturer(s) | Prof.Dr. Selim AKYOKUŞ |
Assistant(s) | |
Aim | Natural language processing (NLP) is a crucial technology in the era of information age. Exciting advancements in natural language processing (NLP) have recently emerged, enabling systems that can perform tasks such as text translation, question answering, and spoken conversations. This course aims to provide students with a foundational understanding of NLP, including standard frameworks, algorithms, and techniques used to solve various NLP problems. The curriculum will cover topics like language modeling, representation learning, text classification, sequence tagging, syntactic parsing, machine translation, and question answering, with a particular focus on recent deep learning approaches. Through this course, students will receive a comprehensive introduction to NLP concepts, methods, algorithms, applications and state-of-the-art methods research in deep learning for NLP. |
Course Content | This course contains; Introduction to Natural Language Processing (NLP),Lingustic Essentials, Regular Exp., Text Normalization, Edit Distance,N-gram Models,Machine Learning Basics, Text Classification, Naive Bayes and Logistic Regression,Vector Semantics and Dense Word Embeddings,Neural Networks and Neural Language Models,Sequence Labeling for Parts of Speech and Named Entities,Exam Week,RNNs and LSTMs,Transformers and Pretrained Language Models, Fine Tuning and Masked Language Models,Machine Translation, Question Answering and Information Retrieval,Chatbots and Dialogue Systems, Automatic Speech Recognition and Text-to-Speech,Context-Free Grammars, Constituency Parsing, Dependency Parsing, Logical Representations of Sentence Meaning,Review and Project Presentations. |
Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
Decompose a real-world problem into subproblems in NLP, use existing natural language processing tools to conduct basic NLP, and identify potential solutions. | 11 | A, F |
Learn about the main uses of machine learning techniques and deep learning models in NLP. | | A, F, G |
Explain state-of-the-art methods to tackle NLP sub-problems, such as text representation, representation learning techniques, text mining, language modeling, and similarity detection, and gain a an understanding about the methods and metrics for various natural language processing tasks and applications. | | A, F, G |
Extract information from text automatically using concepts and methods from natural language processing (NLP) including stemming, n-grams, POS tagging, and parsing. | | A, E, F |
Get familiarized with the terminology, a breadth of concepts and tasks in NLP. | | A, G |
Teaching Methods: | 11: Demonstration Method |
Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task, G: Quiz |
Course Outline
Order | Subjects | Preliminary Work |
---|
1 | Introduction to Natural Language Processing (NLP) | Ch 1 |
2 | Lingustic Essentials, Regular Exp., Text Normalization, Edit Distance | Ch 2 |
3 | N-gram Models | Ch 3 |
4 | Machine Learning Basics, Text Classification, Naive Bayes and Logistic Regression | Ch 4, 5 |
5 | Vector Semantics and Dense Word Embeddings | Ch 6 |
6 | Neural Networks and Neural Language Models | Ch 7 |
7 | Sequence Labeling for Parts of Speech and Named Entities | Ch 8 |
8 | Exam Week | Ch 1-8 |
9 | RNNs and LSTMs | Ch 9 |
10 | Transformers and Pretrained Language Models, Fine Tuning and Masked Language Models | Ch 10, 11 |
11 | Machine Translation, Question Answering and Information Retrieval | Ch 13, 14 |
12 | Chatbots and Dialogue Systems, Automatic Speech Recognition and Text-to-Speech | Sohbet Robotları ve Diyalog Sistemleri, Otomatik Konuşma Tanıma ve Metinden Konuşmaya |
13 | Context-Free Grammars, Constituency Parsing, Dependency Parsing, Logical Representations of Sentence Meaning | Ch 17, 18, 19 |
14 | Review and Project Presentations | |
Resources |
- Speech and Language Processing, D.Jurafsky, J.H.Martin, 3rd Edition, Pearson-Prentice Hall.
- Foundations of Statistical Natural Language Processing, C.D.Manning, H.Schütze, MIT Press, 2002.
- Jacob Eisenstein, Introduction to Natural Language Processing, 2019. |
- Yoav Goldberg. A Primer on Neural Network Models for Natural Language Processing
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning
- Delip Rao and Brian McMahan. Natural Language Processing with PyTorch
- Lewis Tunstall, Leandro von Werra, and Thomas Wolf. Natural Language Processing with Transformers |
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications |
No | Program Qualification | Contribution Level |
1 | 2 | 3 | 4 | 5 |
1 | 1. An ability to apply knowledge of mathematics, science, and engineering | | | X | | |
2 | 2. An ability to identify, formulate, and solve engineering problems | | | | | X |
3 | 3. An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability | | | | X | |
4 | 4. An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice | | | | | X |
5 | 5. An ability to design and conduct experiments, as well as to analyze and interpret data | | | | | X |
6 | 6. An ability to function on multidisciplinary teams | X | | | | |
7 | 7. An ability to communicate effectively | | | | | X |
8 | 8. A recognition of the need for, and an ability to engage in life-long learning | | | X | | |
9 | 9. An understanding of professional and ethical responsibility | | | | X | |
10 | 10. A knowledge of contemporary issues | | | | | |
11 | 11. The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context | | | | X | |
Assessment Methods
Contribution Level | Absolute Evaluation |
Rate of Midterm Exam to Success | | 30 |
Rate of Final Exam to Success | | 70 |
Total | | 100 |
Numerical Data
Ekleme Tarihi: 09/10/2023 - 10:50Son Güncelleme Tarihi: 09/10/2023 - 10:51
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