Syllabus ( CSE 654 )
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Basic information
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| Course title: |
Inroduction to Natural Language and Speech Processing |
| Course code: |
CSE 654 |
| Lecturer: |
Prof. Dr. Yusuf Sinan AKGÜL
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| ECTS credits: |
7.5 |
| GTU credits: |
3 (3+0+0) |
| Year, Semester: |
1/2, Fall and Spring |
| Level of course: |
Second Cycle (Master's) |
| Type of course: |
Area Elective
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| Language of instruction: |
English
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| Mode of delivery: |
Face to face
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| Pre- and co-requisites: |
Data Structures |
| Professional practice: |
No |
| Purpose of the course: |
To teach how to create systems that can understand and produce language, for applications such as information extraction, machine translation, automatic summarization, question-answering, and interactive dialogue systems. |
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Learning outcomes
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Upon successful completion of this course, students will be able to:
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Program known NLP algoritms in C or Java programming languages.
Contribution to Program Outcomes
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Use advanced knowledge of mathematics, science, and engineering
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Continuously develop their knowledge and skills in order to adapt to a rapidly developing technological environment,
Method of assessment
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Written exam
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Homework assignment
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List the fundamental statistical techniques used in NLP.
Contribution to Program Outcomes
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Formulate and solve advanced engineering problems
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Use advanced knowledge of mathematics, science, and engineering
Method of assessment
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Written exam
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Apply known NLP techniques to real world problems.
Contribution to Program Outcomes
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Use advanced knowledge of mathematics, science, and engineering
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Continuously develop their knowledge and skills in order to adapt to a rapidly developing technological environment,
Method of assessment
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Homework assignment
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Contents
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| Week 1: |
Intorduction and course plan |
| Week 2: |
Natural languages and artificial languages |
| Week 3: |
N-grams and language models |
| Week 4: |
Context free grammars |
| Week 5: |
Context free grammars and natural languages |
| Week 6: |
Context free grammars and natural languages |
| Week 7: |
Midterm exam |
| Week 8: |
Semantic analysis |
| Week 9: |
Semantic analysis |
| Week 10: |
Word disambiguation |
| Week 11: |
Summarization |
| Week 12: |
Pronoun resolution |
| Week 13: |
Automated translation |
| Week 14: |
Automated translation |
| Week 15*: |
Natural language generation |
| Week 16*: |
Final exam |
| Textbooks and materials: |
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| Recommended readings: |
Speech and Language Processing , Daniel Jurafsky (Author), James H. Martin (Author) |
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* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
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Assessment
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| Method of assessment |
Week number |
Weight (%) |
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| Mid-terms: |
7 |
30 |
| Other in-term studies: |
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0 |
| Project: |
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0 |
| Homework: |
3,6,9,12 |
30 |
| Quiz: |
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0 |
| Final exam: |
16 |
40 |
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Total weight: |
(%) |
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Workload
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| Activity |
Duration (Hours per week) |
Total number of weeks |
Total hours in term |
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| Courses (Face-to-face teaching): |
3 |
14 |
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| Own studies outside class: |
3 |
14 |
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| Practice, Recitation: |
1 |
14 |
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| Homework: |
10 |
4 |
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| Term project: |
0 |
0 |
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| Term project presentation: |
0 |
0 |
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| Quiz: |
0 |
0 |
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| Own study for mid-term exam: |
20 |
1 |
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| Mid-term: |
2 |
1 |
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| Personal studies for final exam: |
20 |
1 |
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| Final exam: |
3 |
1 |
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Total workload: |
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Total ECTS credits: |
* |
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* ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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