Кто читает:: Международная лаборатория прикладного сетевого анализа

Статус:: Курс по выбору

Когда читается:: 2-й курс, 1, 2 модуль

Abstract

This course focuses on applied methods and existing tools for information retrieval: web scrap-ing, data preprocessing, natural language processing. All methods considered in this course require basic knowledge of discrete mathematics and probabilistic theory. For instance, most NLP and IR methods use conditional probability. In this course, we show the implementation of contemporary approaches in existing software packages (preferably in the python frameworks), and demonstrate how these methods can be used for the solution of some real-world problems.

Learning Objectives

to show the implementation of contemporary approaches in existing software packages (preferably in the python frameworks), and demonstrate how these methods can be used for the solution of some real-world problems.

Expected Learning Outcomes

be able to criticize constructively and determine existing issues with applied nlp tasks
be able to get necessary data for research and applied projects
be able to perform basic ETL operations with datasets and unstructured data
have an understanding of the basic principles of information retrieval
have the skill to meaningfully develop an appropriate data analysis pipeline
have the skill to work unstructured text data
know advantages of existing natural language processing packages
know the basic principles behind the the existing deep learning approaches

Course Contents

IR tasks overview, Python dive in
Web information extraction
Text normalisation
Syntax parsing, fact extraction
Language modelling, text classification and clustering
Sentiment detection
Large Language Models
Machine translation, question answering
Summarization and Domain adaptation
Vector Databases. Semantic search and indexing
Additional topics and course projects defense

Assessment Elements

Homework
Final test
Final Project Defense

Interim Assessment

2024/2025 2nd module
0.4 * Final Project Defense + 0.2 * Final test + 0.4 * Homework

Bibliography

Recommended Core Bibliography

Shay Cohen. (2019). Bayesian Analysis in Natural Language Processing : Second Edition. San Rafael: Morgan & Claypool Publishers. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2102157

Recommended Additional Bibliography

Manning, C. D., & Schèutze, H. (1999). Foundations of Statistical Natural Language Processing. Cambridge, Mass: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=24399

Authors

PARINOV ANDREY ANDREEVICH
Павлова Ирина Анатольевна

Магистерская программа «Аналитика данных и прикладная статистика / Data Analytics and Social Statistics»

Unstructured Data Analysis

Course Syllabus