Natural Language Processing

Bachelor 2022/2023

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'

Category 'Best Course for New Knowledge and Skills'

Type: Compulsory course (Fundamental and Applied Linguistics)

Area of studies: Fundamental and Applied Linguistics

Delivered by: School of Fundamental and Applied Linguistics

Where: Faculty of Humanities (Nizhny Novgorod)

When: 3 year, 2-4 module

Mode of studies: distance learning

Online hours: 20

Open to: students of all HSE University campuses

Instructors: Alexey Malafeev

Language: English

ECTS credits: 5

Contact hours: 64

Full Syllabus

Abstract

The course is aimed at mastering the basics of natural language processing (NLP), a vibrant interdisciplinary field. The course covers the methods and approaches used in many real-world NLP applications such as language modeling, text classification, sentiment analysis, summarization 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 mathematical models underlying modern computational linguistics. The course also involves completing practical programming assignments in Python and conducting experiments on texts written in English and Russian. Pre-requisites are python programming skills, general knowledge of linguistics

Learning Objectives

As a result of mastering the discipline, the student will know the structural features of natural language texts and the principles of their computer processing in order to obtain linguistic (morphological, syntactic, semantic) information;
As a result of mastering the discipline, the student will 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
The student woll understand the limitations of existing computer models of natural language processing

Expected Learning Outcomes

Has an idea of classical and modern approaches to machine translation
Has an idea of computational semantics, knows the basic approaches, able to calculate semantic similarity
Has an idea of different types of summarization and ways to assess the quality of summarization
Has an idea of the classification problem and approaches to it, understands the naive Bayesian classifier algorithm
Has an idea of the tagging problem, knows the principle of hidden Markov models and the basic algorithm for implementation
Knows how to preprocess text, knows the syntax of regular expressions, has an idea of the editing distance.
Knows why language models are needed, knows how to create language models using n-grams
Understands the difference between basic classification metrics
Understands the difference between basic classification metrics, has an idea of dependency and constituent trees and context-free grammars, knows the basic algorithms of syntactic parsing
Understands the difficulties in natural language processing, has an idea of approaches to solve these problems

Course Contents

Introduction to natural language processing
Basic text processing and edit distance
Language models
Tagging problems and hidden Markov models
Text classification and sentiment analysis
Evaluation
Parsing
Machine translation
Computational semantics
Text summarization

Assessment Elements

Classroom performance
Graded test
Exam

Interim Assessment

2022/2023 3rd module
0.5 * Classroom performance + 0.5 * Graded test
2022/2023 4th module
0.5 * Exam + 0.5 * Classroom performance

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.

Recommended Additional Bibliography

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

Course Syllabus