Бакалавриат
2025/2026
Компьютерная лингвистика
ID 1011782
Статус:
Курс обязательный (Прикладная математика и информатика)
Когда читается:
4-й курс, 3 модуль
Охват аудитории:
для всех кампусов НИУ ВШЭ
Преподаватели:
Воеводкин Вадим Сергеевич
Язык:
английский
Кредиты:
6
Контактные часы:
40
Course Syllabus
Abstract
The course is aimed at training specialists capable of conducting information modeling of subject areas and solving applied information processing tasks at a high technical level. Practical classes serve to develop stable skills in natural language processing using modern high-level programming languages as an application programmer.
To complete the assignments, the Python3 scripting language is used, as well as the Anaconda4 technology platform. To master the discipline, students must possess the following knowledge and competencies:
• modern methods of designing and implementing information systems;
• basic algorithms and data structures for fast information retrieval;
• programming in C and C++.
Learning Objectives
- The objectives of mastering the discipline "Computational Linguistics" are the formation of students' clear understanding of the place and role of modern data extraction systems, mastering the theoretical foundations of modeling and processing information in natural language, understanding the trends in the development of the industry and the direction of prospective research, students' study of the principles of building modern information retrieval systems
Expected Learning Outcomes
- Be able to process texts using basic algorithms
- Be able to use vector representations of texts to answer queries
- Be able to use a probabilistic model to search for information in the text
Course Contents
- Basics of text processing
- Transformer models and their applications to various natural language processing tasks
- Language modeling and text representation methods
Assessment Elements
- Laboratory work 2. Processing and classification of texts
- Laboratory work 4. Generating texts using a neural network language model
- Laboratory work 1. Collection of text corpus
- Laboratory work 3. Topic Modeling
- Laboratory work 5. Modern problems of language analysis
- ExamTickets, theory included, + additional questions/tasks
Interim Assessment
- 2025/2026 3rd module0.16 * Laboratory work 1. Collection of text corpus + 0.2 * Exam + 0.16 * Laboratory work 5. Modern problems of language analysis + 0.16 * Laboratory work 3. Topic Modeling + 0.16 * Laboratory work 2. Processing and classification of texts + 0.16 * Laboratory work 4. Generating texts using a neural network language model
Bibliography
Recommended Core Bibliography
- Derivatives analytics with Python : data analysis, models, simulation, calibration and hedging, Hilpisch, Y. J., 2015
Recommended Additional Bibliography
- Image analysis, classification, and change detection in remote sensing : with algorithms for Python, Canty, M. J., 2019
- Learning Python : [covers Python 2.5], Lutz, M., 2008