Магистратура 2019/2020

Основы компьютерной лингвистики

Лучший по критерию «Новизна полученных знаний»

Статус: Курс обязательный (Политическая лингвистика)

Направление: 45.04.03. Фундаментальная и прикладная лингвистика

Кто читает: Кафедра прикладной математики и информатики (Нижний Новгород)

Где читается: Факультет гуманитарных наук (Нижний Новгород)

Когда читается: 1-й курс, 1 модуль

Формат изучения: без онлайн-курса

Преподаватели: Дурандин Олег Владимирович

Прогр. обучения: Политическая лингвистика

Язык: английский

Кредиты: 4

Контактные часы: 28

Course Syllabus

Abstract

The course is aimed at mastering the basics of natural language processing (NLP) and computational linguistics (CL) vibrant interdisciplinary fields. The course covers the methods and approaches used in many real-world NLP applications such as language modeling, text classification, sentiment analysis and machine translation. The students taking the course will not only use some of the existing NLP libraries and software packages, but also learn about the principles behind their design, and about the main mathematical models underlying modern computational linguistics. The course also involves completing some practical programming assignments in Python and conducting experiments on texts written in English and Russian.

Learning Objectives

• Know the structural features of natural language texts and the principles of their computer processing in order to obtain linguistic (morphological, syntactic, semantic) information;
• Have an idea of the methods used to solve complex practical problems of natural language processing, in particular, information retrieval, summarization, sentiment analysis, machine translation;
• Understand the limitations of existing computer models of natural language processing.

Expected Learning Outcomes

• Will be able to understand modern tasks of NLP, understand terminology
• Understand basic methodology of text processing
• Will be able to create basic regular expression for everyday tasks
• Understanding terminology of language models. Will be able to apply which language model to use and the cases of using LM.
• Understand task of PoS-tagging.
• Ability of use modern PoS-taggers for Russian and English languages
• Understand task of text classification
• Will be able to choose right algorithms for text classification
• Use text classification algorithms for sentiment analysis and text categorization
• Understand pros and cons evaluation methods in data mining tasks
• Will be able to interpret metrics in ML tasks
• Understand basic algorithms of machine translation. Understand task of machine translation
• Understand terminology of computational semantics. Onthology (WordNet) and distributional semantics,

Course Contents

• Introduction to natural language processing
  Structural features of texts in natural language; ambiguity on all levels of language; the main challenges of natural language processing; basic approaches to problem solving: manually written rules and machine learning.
• Basic text processing and edit distance
  Preprocessing: tokenization and segmentation; normalization of words: stemming, lemmatization, morphological analyzers; regular expressions; edit distance.
• Language models
  N-grams; perplexity; methods of smoothing; the use of language models: input prediction, error correction, speech recognition, text generation.
• Tagging problems and hidden Markov models
  POS tagging; named entity recognition as a tagging problem; hidden Markov models, their ad-vantages and disadvantages; the Viterbi algorithm.
• Text classification and sentiment analysis
  Classification problems; naive Bayes classifier; text classification; sentiment analysis.
• Evaluation
  Performance measures: accuracy, precision, recall, F-measure; state-of-the-art.
• Basic of machine translation
  Classical approaches: direct, transfer-based, interlingual; statistical machine translation; IBM model; alignment; phrase-based translation models.
• Computational semantics
  Word senses and meanings; WordNet; semantic similarity measures: thesaurus-based and distri-butional methods.

Assessment Elements

• Тест
• Тест
• Тест
• Тест
• Тест
• Устный экзамен

Interim Assessment

• Interim assessment (1 module)
  0.1 * Тест + 0.1 * Тест + 0.1 * Тест + 0.1 * Тест + 0.1 * Тест + 0.5 * Устный экзамен

Bibliography

Recommended Core Bibliography

• Perkins, J. Python Text Processing with NLTK 2.0 Cookbook: Use Python NLTK Suite of Libraries to Maximize Your Natural Language Processing Capabilities [Электронный ресурс] / Jacob Perkins; DB ebrary. – Birmingham: Packt Publishing Ltd, 2010. – 336 p.
• The Handbook of Natural Language Processing [Электронный ресурс] / edited by Robert Dale, Hermann Moisl, Harold Somers; DB ebrary. – New York: Marcel Dekker, Inc., 2010. – XIX, 996 p. – режим доступа: https://ebookcentral.proquest.com/lib/hselibrary-ebooks/reader.action?docID=216282&query=natural+language+processing+with

Recommended Additional Bibliography

• Sarkar, D. Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data [Электронный ресурс] / Dipanjan Sarkar; БД Books 24x7. – Chicago: Apress, 2016. – 412 p. – ISBN 978-1-4842-2387-1
• The Handbook of Computational Linguistics and Natural Language Processing [Электронный ресурс] / ed. by Alexander Clark, Chris Fox, Shalom Lappin; DB ebrary. – Chichester: John Wiley & Sons, 2013. – 203 p. – Режим доступа: https://ebookcentral.proquest.com/lib/hselibrary-ebooks/reader.action?docID=4035461&query=computational+linguistics