Program Technologies of Natural Language Processing (ANLP)
Course: «Applied Linguistics (Translation Editing and Expert Linguistic Analysis)»
Structural unit: Educational and Scientific Institute of Philology
Title
Program Technologies of Natural Language Processing (ANLP)
Code
ДВС.1.05
Module type
Вибіркова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2022/2023
Semester/trimester when the component is delivered
4 Semester
Number of ECTS credits allocated
4
Learning outcomes
PLO 23.1. To evaluate and choose the methods and models of development, implementation, and operation program means and control them at all stages of the life cycle.
PLO 24.1. Developing and evaluating strategies to design the program means; justifying, analyzing, and evaluating the adopted project solutions in terms of the final program product quality.
PLO 25.1. To choose paradigms and programming languages to solve the Applied Linguistics problems, apply the system and specialized tools, component technologies (platforms), and integrated environments of software development.
PLO 27.1
Form of study
Full-time form
Prerequisites and co-requisites
Basic programming and database skills. Understanding the concept and basic principles of natural language processing systems.
Course content
The educational discipline is devoted to the study of issues necessary for the high-quality implementation of relevant information systems for the automatic processing of natural language: working with API libraries; working with databases of linguistic information; creating electronic learning tools and automated learning systems for natural languages.
To implement practical tasks, the Python programming language, PyCharm visual software development environment, Telegram API library, SQLite database management system, etc. are studied.
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. 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.
4. Vajjala S. Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems / Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta & Harshit Surana. – Sebastopol, CA, US : O'Reilly Media, 2020. – 456 p.
5. Jurafsky D. Speech and Language Processing / Daniel Jurafsky, James H. Martin, Peter Norvig, Stuart Russell. – London : Pearson, 2014. – 1032 p.
Planned learning activities and teaching methods
Lectures, laboratory, and practical classes, independent work.
Assessment methods and criteria
Assessment methods: performing laboratory work, writing a modular test, and passing computer testing.
The credit is given based on the results of the student's work during the entire semester and does not include additional assessment measures. Students who scored the minimum allowable number of points (60) receive a "Passed". Students who did not score the minimum allowable number of points (60) receive a "Failed". The scale of conformity of grades: Passed - 60-100; Failed - 0-59.
Language of instruction
Ukrainian, 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