European Summer School in Logic, Language and Information- ESSLI 2025 Reading Concordances

Reading Concordances: A Training Course in Key Corpus Linguistics Methodology

📅 Dates: 4–8 August 2025, 9:00–11:00 AM

📍 Part of: ESSLLI 2025 (Week 2)

🔗 More Info: ESSLLI 2025 Homepage

Instructors

Abstract

Concordance analysis via a KWIC (Key Word In Context) display is a mainstay of contemporary corpus linguistics, serving as a bridge between qualitative and quantitative approaches to the study of text. This course offers an introduction to “concordance reading”, i.e. analysis of concordance data supported by computational algorithms. We begin by situating concordance reading in the wider context of qualitative-quantitative research. We introduce key concepts for the description of patterns in concordances (e.g. collocations and colligations) as well as different examples of concordance software (e.g. AntConc, CLiC, and CQPweb). We then focus on specific concordance reading strategies, such as selecting, sorting, and grouping concordance lines, providing formal definitions and corresponding computational algorithms. Participants will gain hands-on experience working with FlexiConc, a computational library for concordance analysis to be released in autumn 2024, and other concordance tools. We will give participants the opportunity to consider the potential of concordance reading for their own research contexts.

This Course Aims to:

  1. Introduce the fundamental concepts of reading concordances and well-known concordance software tools.
  2. Explain the general strategies of selecting, ordering, and grouping concordance lines that are used to organise concordances for interpretation, as well as some other less common strategies.
  3. Describe computational algorithms that support these strategies, with formal definitions and mathematical properties.
  4. Offer practical examples and hands-on experience how the concordance reading strategies and corresponding algorithms can be applied to different research questions.
  5. Introduce analysis trees as a powerful tool for research documentation and reproducibility in concordance analysis workflows.

Tentative Outline

1. Introduction to Reading Concordances

  • Concordance: A definition
  • Introductory concordance examples
  • Finding patterns in concordances
  • Overview of concordance software and its functionalities
  • Application examples from different fields
    1. Lexicography
    2. Data-driven learning
    3. Corpus assisted discourse studies
    4. Literary studies and DH

2. Strategies for Organising Concordances

  • Organising concordances for interpretation
  • General strategies of selecting, ordering, and grouping
  • Purpose of each strategy and their combination
  • Overview of the use of these strategies in published research
  • Hands-on exercise: Apply concordance reading strategies

3. Computational Algorithms

  • Classification of computational algorithms for organising concordances according to the three general strategies
  • Mathematical properties of algorithms and their combination
  • Examples of well-known and more specialised algorithms
  • Hands-on exercise: Try out a wide range of algorithms via the FlexiConc Python library and its demonstrator Web app

4. Reproducibility and Research Documentation

  • Reproducibility & documentation as challenges for concordance analysis
  • The analysis tree as an approach for documenting and reproducing the process of organising a concordance
  • Hands-on exercise (long): Carry out concordance analyses on topics from different research fields, documenting them in the form of an analysis tree

5. Summary & Outlook

  • Concordance reading: state of the art and perspectives for the future
  • Other strategies for organising concordances (e.g. collocations & networks)
  • Integrating concordances with quantitative and statistical approaches
  • Concordance reading in your own research
  • Final discussions, with feedback & suggestions from participants

Expected Level and Prerequisites

The course is intended for students and young researchers with a general background in linguistics, computational linguistics, computational social science, digital humanities, computer science, or related fields. No prior knowledge of corpus linguistics or specific tools is required, but a general familiarity with computer-assisted analysis of text data will be beneficial.

For further details, visit:

🌐 ESSLLI 2025 Course Schedule

🌐 ESSLLI 2025 Homepage