Subject

Natural language processing

Course Title

Natural language processing
Natural language understanding and generation

Code

F23L3W142

Study Programme

—

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

First Cycle

Academic year / semester

5 / Winter

Number of ECTS credits

Teacher

Ivica Dimitrovski, Sonja Gievska

Prerequisites for enrolling in the course

Algorithms and Data Structures or Applied Algorithms and Data Structures

10.

Objectives of the course programme (competences)

The goal of the course is for students to acquire the fundamental theoretical and practical knowledge of natural language processing algorithms. Students will gain knowledge of the latest machine learning techniques with a focus on deep neural networks designed for text understanding and generation.

11.

Course content

1. Introduction. Basic Natural Language Processing.
2. Vector representation of words
3. Modeling Natural Languages with Deep Neural Networks
4. Overview of deep neural architectures. Knowledge extraction from textual data
5. Machine translation
6. Text Generation
7. Learning Transfer. Pre-trained Models
8. Question Answering and Text Summarization Systems
9. Enriched representation and enriched models of natural languages (integration of knowledge bases, knowledge graph)
10. Re-examination of models from the perspective of ethical and moral norms
11. Dialogue Management Systems
12. Re-examination and analysis of models for understanding and generating text (interpreting what has been learned)

12.

Learning methods

Lectures supported by slide presentations, interactive lectures, exercises (using equipment and software packages), teamwork, case studies, guest lecturers, independent preparation and defense of a project assignment and a seminar paper, and learning in an electronic environment (forums, consultations).

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

30 + 45 + 15 + 15 + 75 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	30 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	45 hours

16.

Other forms of activities

16.1.	Project assignments	15 hours
16.2.	Independent assignments	15 hours
16.3.	Home study	75 hours

17.

Assessment method

17.1.	Tests	10 points
17.2.	Seminar paper / project (presentation: written and oral)	15 points
17.3.	Activities and learning	10 points
17.4.	Final exam	70 points

18.

Grading criteria (points / grade)

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.

Requirement for obtaining a signature and taking the final exam

completed activities 15.1 and 15.2

20.

Language of instruction

Macedonian and English

21.

Method for monitoring the quality of teaching

internal evaluation and survey mechanism

22.

Literature

22.1.	Required literature 1. Jurafsky & Martin \| Speech and Language Processing \| Prentice Hall \| 2021 2. Ian Goodfellow, Joshua Bengio, Aaron Courville \| Deep Learning \| MIT Press \| 2016
22.2.	Additional literature No. Author Title Publisher Year