| 1. | Course Title | Natural Language Processing | |||||||||||
| 2. | Code | 4ФЕИТ05032 | |||||||||||
| 3. | Study program | 19-MV | |||||||||||
| 4. | Organizer of the study program (unit, institute, department) | Faculty of Electrical Engineering and Information Technologies | |||||||||||
| 5. | Degree (first, second, third cycle) | Second cycle | |||||||||||
| 6. | Academic year/semester | I/1 | 7. | Number of ECTS credits | 6.00 | ||||||||
| 8. | Lecturer | Dr Branislav Gerazov | |||||||||||
| 9. | Course Prerequisites | ||||||||||||
| 10. | Course Goals (acquired competencies):
The goal of the course program is to allow students to acquire a wide knowledge of the techniques for the processing of natural language. It is designed to bring close the various approaches and applications through studying the state-of-the-art. |
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| 11. | Course Syllabus:
1. Introduction to Natural Language Processing 2. Language modelling 3. Text classification and Sentiment analysis 4. Neural Networks for NLP 5. Distributional Hypothesis and Word Embeddings 6. POS tagging 7. Syntax 8. Dependency Parsing 9. Semantics 10. Machine Translation 11. Language Generation 12. Information Extraction 13. Dialogue generation |
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| 12. | Learning methods:
Combined learning: lectures with slides and visualisations and independent work on projects. |
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| 13. | Total number of course hours | 180 | |||||||||||
| 14. | Distribution of course hours | 3 + 3 | |||||||||||
| 15. | Forms of teaching | 15.1 | Lectures-theoretical teaching | 45 hours | |||||||||
| 15.2 | Exercises (laboratory, practice classes), seminars, teamwork | 45 hours | |||||||||||
| 16. | Other course activities | 16.1 | Projects, seminar papers | 30 hours | |||||||||
| 16.2 | Individual tasks | 30 hours | |||||||||||
| 16.3 | Homework and self-learning | 30 hours | |||||||||||
| 17. | Grading | ||||||||||||
| 17.1 | Exams | 0 points | |||||||||||
| 17.2 | Seminar work/project (presentation: written and oral) | 50 points | |||||||||||
| 17.3. | Activity and participation | 20 points | |||||||||||
| 17.4. | Final exam | 30 points | |||||||||||
| 18. | Grading criteria (points) | up to 50 points | 5 (five) (F) | ||||||||||
| from 51 to 60 points | 6 (six) (E) | ||||||||||||
| from 61 to 70 points | 7 (seven) (D) | ||||||||||||
| from 71 to 80 points | 8 (eight) (C) | ||||||||||||
| from 81 to 90 points | 9 (nine) (B) | ||||||||||||
| from 91 to 100 points | 10 (ten) (A) | ||||||||||||
| 19. | Conditions for acquiring teacher’s signature and for taking final exam | Attendance to lectures. | |||||||||||
| 20. | Forms of assessment | Project assignment and final exam. | |||||||||||
| 21. | Language | Macedonian and English | |||||||||||
| 22. | Method of monitoring of teaching quality | Surveys, interviews and self-evaluation. | |||||||||||
| 23. | Literature | ||||||||||||
| 23.1. | Required Literature | ||||||||||||
| No. | Author | Title | Publisher | Year | |||||||||
| 1. | Dan Jurafsky and James H. Martin | Speech and Language Processing | Pearson Education | 2014 | |||||||||
| 2. | Yoav Goldberg | Neural Network Methods for Natural Language Processing | Morgan & Claypool Publishers | 2017 | |||||||||
| 23.2. | Additional Literature | ||||||||||||
| No. | Author | Title | Publisher | Year | |||||||||
| 1. | Uday Kamath, John Liu, James Whitaker | Deep Learning for NLP and Speech Recognition | Springer | 2019 | |||||||||
| 2. | Ian Goodfellow, Yoshua Bengio and Aaron Courville | Deep Learning | MIT Press | 2016 | |||||||||
| 3. | Hobson Lane, Cole Howard, Hannes Hapke | Natural Language Processing in Action: Understanding, analyzing, and generating text with Python | Manning Publications | 2019 | |||||||||
