Магистратура 2020/2021

Обработка естественного языка на Python

Статус: Курс по выбору (Науки о данных)

Направление: 01.04.02. Прикладная математика и информатика

Кто читает: Кафедра технологий моделирования сложных систем

Где читается: Факультет компьютерных наук

Когда читается: 1-й курс, 3, 4 модуль

Формат изучения: без онлайн-курса

Преподаватели: Рыгаев Иван Петрович

Прогр. обучения: Науки о данных

Язык: английский

Кредиты: 4

Контактные часы: 52

Course Syllabus

Abstract

The course will cover the main tools and libraries of the Python language for natural language processing (NLTK - natural language toolkit). Tokenization, stemming, morphological and syntactic parsing, working with semantics, access to corpora and lexical resources, text classification, search for named entities.

Learning Objectives

• Get practical skills in using the Natural Language Toolkit library in Python for natural language processing tasks.

Expected Learning Outcomes

• Be able to use Python tools to access corpora and lexical resources
• Be able to use Python tools for processing raw text
• Be able to use Python tools to categorize and mark up words
• Be able to use Python tools to classify texts
• Be able to use Python tools to extract information from text
• To be able to apply Python tools for syntactic analysis of sentences
• Be able to use Python tools for semantic text analysis

Course Contents

• Language Processing and Python
  General information about computer-generated natural language processing and the NLTK library.
• Access to text corpora and lexical resources
  Learning Python tools for working with corpora and lexical resources
• Raw Text Processing
  Learning Python tools for raw text processing
• Categorization and markup of words
  Learning Python tools for morphological analysis, categorization, and markup of words in the text
• Classification of texts
  Learning Python tools for the genre, style, and other text categorization.
• Extracting information from text
  Learning Python tools for extracting information from text
• Analysis of the syntactic structure of a sentence
  Learning Python tools for parsing natural language sentences.
• Analysis of the meaning of the sentence
  Learning Python tools for semantic analysis of texts, the representation of the meaning of a sentence.

Assessment Elements

• Домашнее задание
• Опять домашнее задание
  The score for the discipline is set in accordance with the evaluation formula from all the passed control elements. The exam is not held.

Interim Assessment

• Interim assessment (4 module)
  0.5 * Домашнее задание + 0.5 * Опять домашнее задание

Bibliography

Recommended Core Bibliography

• Lutz, M. (2006). Programming Python (Vol. 3rd ed). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=415084
• Perkins J. Python text processing with NLTK 2.0 cookbook. – Packt Publishing Ltd, 2010. – 336 pp.
• Perkins, J. (2014). Python 3 Text Processing with NLTK 3 Cookbook. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=836632

Recommended Additional Bibliography

• Perkins, J. (2010). Python Text Processing with NLTK 2.0 Cookbook : Over 80 Practical Recipes for Using Python’s NLTK Suite of Libraries to Maximize Your Natural Language Processing Capabilities. Packt Publishing.