Multi-semester discipline Programming Technologies within Linguistics. Part 1, 2

Course: «Applied (computer) Linguistics and English language»

Structural unit: Educational and Scientific Institute of Philology

Title
Multi-semester discipline Programming Technologies within Linguistics. Part 1, 2
Code
ДВС 1.06
Module type
Вибіркова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2022/2023
Semester/trimester when the component is delivered
6 Semester
Number of ECTS credits allocated
4
Learning outcomes
PLO 24. To know and to implement fundamental conceptions, paradigms, and the main functioning principles of language, instrumental and computing means of software engineering. PLO 25. To conduct a pre-project inspection of a linguistic branch and system analysis of the linguistic object of software designing. PLO 26. To be able to choose and use methodology for creating software in accordance with a linguistic task. PLO: 29, 30, 31, 32.
Form of study
Full-time form
Prerequisites and co-requisites
1) Having basic programming skills; 2) understanding the main tasks of computational linguistics.
Course content
The educational discipline is devoted to the study of issues necessary for solving the tasks of computational linguistics, in particular, the creation of console projects for linguistic applications; using libraries and creating functions for natural language processing; working with files and databases of linguistic information. To implement practical tasks in the discipline, the Python programming language, the visual software development environment IntelliJ PyCharm, the SQLite database management system, etc. are studied in particular.
Recommended or required reading and other learning resources/tools
1. Bird S. Natural Language Processing with Python [Електрон. ресурс] / Steven Bird, Ewan Klein, and Edward Loper. – 2020. – URL : https://www.nltk.org/book. – Дата звернення: 12.10.2020. 2. Lane H., Howard C., Hapke H. M. Natural Language Processing in Action: Understanding, analyzing, and generating text with Python. – Shelter Island : Manning, 2019. – 544 p. 3. Joshi P. Artificial Intelligence with Python. – Birmingham - Mumbai : Packt, 2017. – 446 p. 4. Deitel P., Deitel H. Python for Programmers. – London : Pearson Education, 2019. – 640 p. 5. Bengfort B., Bilbro R., Ojeda T. Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning. – Beijing; Boston; Farnham; Sebastopol; Tokyo : O'Reilly, 2018. – 332 p.
Planned learning activities and teaching methods
Lectures, laboratory classes, independent work, laboratory work, and a modular control test.
Assessment methods and criteria
At the end of the second semester, the assessment is conducted. The arithmetic average of the sum of points scored by the student for completed tasks during the 1st and 2nd semesters of studying the discipline is calculated for the credit and is presented as a final assessment. Students who scored the minimum allowable number of points (60) get"Passed". Students who did not score the minimum allowable number of points (60) get "Failed".
Language of instruction
Ukrainіаn, English

Lecturers

This discipline is taught by the following teachers


Faculty of information Technology

Departments

The following departments are involved in teaching the above discipline

Faculty of information Technology