Проектно-исследовательский семинар "Современные цифровые технологии текстовой аналитики"

Магистратура 2022/2023

Статус: Курс обязательный (Прикладная лингвистика и текстовая аналитика / Applied Linguistics and Text Analytics)

Направление: 45.04.03. Фундаментальная и прикладная лингвистика

Кто читает: Департамент фундаментальной и прикладной лингвистики

Где читается: Факультет гуманитарных наук (Нижний Новгород)

Когда читается: 2-й курс, 1-3 модуль

Формат изучения: без онлайн-курса

Охват аудитории: для своего кампуса

Преподаватели: Меркулова Эдита Николаевна, Сохраби Маджид, Товкес Мария Юрьевна

Прогр. обучения: Прикладная лингвистика и текстовая аналитика

Язык: английский

Кредиты: 4

Контактные часы: 72

Full Syllabus

Abstract

In the Project Seminar Modern Digital Technologies of Text Analytics (2nd year) various modern methods and techniques are studied. Multiple text types (including multimodal, different genres) are tackled with the use of software. Programming using R and Python is applied to work with Big Data. The aim of the seminar is to revise and evaluate and put into practice familiar as well as brand-new approaches.

Learning Objectives

the development of skills based on qualitative and quantitative methods and techniques that allow to evaluate the characteristics of various communicative situations of discourse; the development of skills concerning corpus and other computer methods and techniques that allow to evaluate the characteritics of various communicative situations of discourse; the development of skills using qualitative and quantitative methods and techniques that allow to evaluate individual style features (idiostyle features) of communicants in discourse; training of masters , to do independent research design and organizational work.

Expected Learning Outcomes

* Get value out of Big Data by using a 5-step process to structure your analysis.
Knowledge about how to design, develop and evaluate NLP programs using programming language Python
Knowledge about ongoing developments in NLP
A student compares research design
Affective mechanisms of decision-making
Able to think critically and interpret the experience (personal and of other persons), relate to professional and social activities
Able to solve professional problems based on synthesis and analysis
Known basic NLP tasks

Course Contents

NLP
Data Visualization
Academic project (Master’s thesis)

Assessment Elements

домашнее задание
Students accomplish the assignments given
домашнее задание
домашнее задание

Interim Assessment

2022/2023 3rd module
0.3 * домашнее задание + 0.4 * домашнее задание + 0.3 * домашнее задание

Bibliography

Recommended Core Bibliography

Academic English: Theoretical and Practical Issues : учеб. пособие / сост. Т.Ю. Мкртчян, М.Г. Науменко ; Южный федеральный университет. - Ростов-на-Дону ; Таганрог : Издательство Южного федерального университета, 2018. - 165 с. - ISBN 978-5-9275-2853-0. - Режим доступа: https://new.znanium.com/catalog/product/1039713
Alblawi, A. S. (2018). Big Data and Learning Analytics in Higher Education: Demystifying Variety, Acquisition, Storage, NLP and Analytics.
Aman Kedia, & Mayank Rasu. (2020). Hands-On Python Natural Language Processing : Explore Tools and Techniques to Analyze and Process Text with a View to Building Real-world NLP Applications. Packt Publishing.
Rukmini, S. (2017). Academic Research Writing: An Overview and Process. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.7CED46CD

Recommended Additional Bibliography

Hellmann, S. (2015). Integrating Natural Language Processing (NLP) and Language Resources Using Linked Data.
I. Korotkina B., & И. Короткина Б. (2017). Academic Literacy and Methods of Global Scientific Communication ; Академическая грамотность и методы глобальной научной коммуникации. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.8C60FBE4

Course Syllabus