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Regular version of the site
Bachelor 2017/2018

Computer Tools for Linguistic Research

Category 'Best Course for Career Development'
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 Applied Linguistics and Foreign Languages
When: 2 year, 3, 4 module
Mode of studies: Full time
Instructors: Alexey Malafeev
Language: English
ECTS credits: 4

Course Syllabus

Abstract

The course is aimed at imparting to students knowledge of modern computer tools and resources used in research in the fields of corpus, applied and computational linguistics, as well as teaching students to apply these tools and resources to linguistic problems. The computer tools covered in this course in-clude: concordancers, corpus managers, corpus-building (and bootstrapping) tools, lemmatizers, stemmers, morphological analyzers, part-of-speech taggers, syntactic and semantic taggers, regular expressions, as well as the text-processing capabilities of the Python programming language. The course involves conducted individual and group research and presenting the results to the class. Pre-requisites: basic Python programming skills, general knowledge of linguistics
Learning Objectives

Learning Objectives

  • The discipline is aimed at students' acquiring knowledge about current computer tools and resources used by linguists in research in the field of corpus, applied and computer linguistics, as well as practical skills in the use of these tools. Computer tools studied within the discipline include concordancers, corpus managers, programs for automatic corpus creation, lemmatizers, stemmers, morphological analyzers and automatic text markup, regular expressions, and Python programming language tools for processing text data.
Expected Learning Outcomes

Expected Learning Outcomes

  • Understands the basic concepts of corpus linguistics, knows types and properties of corpora, able to obtain concordance. Understands the idea of using the web as a corpus, familiar with the criticism of corpus linguistics
  • Has an idea of the periods of development of corpus linguistics, familiar with the main corpus of English
  • Familiar with the main stages of corpus preprocessing, able to build a corpus (manually and automatically)
  • Has an idea of Cipf's law, able to visualize syntax trees, use regular expressions, works with web interfaces of popular corpora, able to make corpora based on the web and explore ready corpora in AntConc
  • Familiar with corpora of the Russian language
Course Contents

Course Contents

  • Introduction to corpus linguistics
    Basic concepts of corpus linguistics. Text and corpus. Corpus linguistics as a discipline. Types and properties of corpora. Web as a corpus. The use of corpora. The value of corpora. Corpora and computa-tional linguistics. Markup. Concordance, concordancer. Criticism of corpus linguistics.
  • History and typology of English-language corpora
    Periods of corpora history. First machine-readable corpora. The Brown Corpus. Syntactic treebanks. The Penn Treebank. The British National Corpus. The International Corpus of English. The Corpus of Contemporary American English. TenTen Corpora. The Google Books Ngram Corpus. Semantic treebanks. FrameNet. Groningen Meaning Bank.
  • Building corpora
    Corpus design. Stages of corpus compilation. Text processing: tokenization, lemmatization, stem-ming, parsing. Tagging. Copyright. Standardization. Bootstrapping.
  • The corpora of the Russian language
    Uppsala corpus of the Russian language. Tübingen corpus of Russian texts. Computer corpus of texts of Russian Newspapers of the late XX century. Large Corpus of Russian language. Machine Fund of the Russian language. Corpus of Russian literary language. HANKO. NOTHING. OpenCorpora.
  • Computer tools review. Using corpora
    Zipf’s law. Visualization of syntax trees with phpSyntaxTree. Web interfaces to corpora (COCA, RNC). Regular expressions. AntConc. Sketch Engine. Games with a purpose. BootCaT.
Assessment Elements

Assessment Elements

  • non-blocking Control work
  • non-blocking Oral exam
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.5 * Control work + 0.5 * Oral exam
Bibliography

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

  • - Грудева Е.В. — Корпусная лингвистика: учебное пособие - Издательство "ФЛИНТА" - 2017 - ISBN: 978-5-9765-1497-3 - Текст электронный // ЭБС Лань - URL: https://e.lanbook.com/book/106859