5.00 credits
30.0 h
Q2
Teacher(s)
Vincent Arnaud;
Language
English
Prerequisites
The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.
Learning outcomes
At the end of this learning unit, the student is able to : | |
| - situate corpus-assisted discourse studies (CADS) within the wider domain of computer-assisted text analysis and vis-à-vis other methods and disciplines such as text mining, sentiment analysis, critical discourse analysis (CDA), culturomics, and corpus linguistics ; - identify the main strengths and weaknesses of the aforementioned methods and disciplines ; - read, describe and critically review research articles pertaining to the fields of discourse analysis, critical discourse analysis and corpus-assisted discourse analysis ; - master some basic principles of quantitative reasoning and evidence-based research ; - report quantitative data according to the generally-accepted standards ; - use ready-made corpora and build their own DIY corpora ; - undertake concordance analyses, collocation/collocation network analyses, dispersion/distribution analyses and keyword analyses; - extract keywords/N-grams/key N-grams for the purpose of plagiarism detection and idiolect identification ; - develop their own research question and make an informed choice of tools, methods and theoretical concepts to serve their study ; - report and discuss (in writing and orally) their research question, methods, and results ; - identify the main limitations of their research project |
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Content
The course will be split between theoretical sessions, hands-on sessions, and flipped classroom activities.
The course will be divided into 8 main chapters :
Chapter 1 : setting the scene (situating CADS within the wider domain of computer-assisted text analysis)
Chapter 2 : collocations
Chapter 3 : frequency and dispersion
Chapter 4 : concordances
Chapter 5 : N-Grams and plagiarism detection
Chapter 6 : keywords
Chapter 7 : Key N-Grams
Chapter 8 : Wrapping things up
The course will be divided into 8 main chapters :
Chapter 1 : setting the scene (situating CADS within the wider domain of computer-assisted text analysis)
Chapter 2 : collocations
Chapter 3 : frequency and dispersion
Chapter 4 : concordances
Chapter 5 : N-Grams and plagiarism detection
Chapter 6 : keywords
Chapter 7 : Key N-Grams
Chapter 8 : Wrapping things up
Teaching methods
Lectures, hands-on sessions, flipped classrooms, case-studies and group activities, discussions, interactive quizzes.
Evaluation methods
Formative assessment:
The various activities and exercises organised during the class sessions should make it possible to evaluate the students' progress.
Certificate-based assessment:
The course will be evaluated on the basis of the following activities :
Class-session activities: 30% (The students will be informed as to which activities count towards the 30%)
Research project and presentation (Individual term paper): 70%
The various activities and exercises organised during the class sessions should make it possible to evaluate the students' progress.
Certificate-based assessment:
The course will be evaluated on the basis of the following activities :
Class-session activities: 30% (The students will be informed as to which activities count towards the 30%)
Research project and presentation (Individual term paper): 70%
Other information
Use of generative artificial intelligence (or any other online tool, e.g. translators, spelling and grammar checkers, ...)
If the student chooses to use one or more AIs (or any other online tool), they must systematically indicate all the parts in which these tools were used, e.g. in footnotes. The student should specify whether the AI was used to search for information, to write the text, or to improve or correct it. The student should also mention which AI (or other online tool) was used (ChatGPT, Bing, Bard, Chatsonic, DeepL, etc.) and the date on which it was used. Information sources must be systematically cited in accordance with bibliographic referencing standards. The student remains responsible for the content of their work, regardless of the sources used.
In order to ensure that the student's written work is personal, criteria such as originality, critical thinking, creativity and illustration with examples (e.g. from their own experience) will be taken into account.
Any behavior on the part of the student that prevents or attempts to prevent, in whole or in part, the correct assessment of their knowledge, skills and/or competences will be considered an irregularity that may lead to sanctions.
If the student chooses to use one or more AIs (or any other online tool), they must systematically indicate all the parts in which these tools were used, e.g. in footnotes. The student should specify whether the AI was used to search for information, to write the text, or to improve or correct it. The student should also mention which AI (or other online tool) was used (ChatGPT, Bing, Bard, Chatsonic, DeepL, etc.) and the date on which it was used. Information sources must be systematically cited in accordance with bibliographic referencing standards. The student remains responsible for the content of their work, regardless of the sources used.
In order to ensure that the student's written work is personal, criteria such as originality, critical thinking, creativity and illustration with examples (e.g. from their own experience) will be taken into account.
Any behavior on the part of the student that prevents or attempts to prevent, in whole or in part, the correct assessment of their knowledge, skills and/or competences will be considered an irregularity that may lead to sanctions.
Bibliography
Les présentations ppt, les exercices, les articles scientifiques et les autres supports de cours seront mis à disposition sur le site du cours.
Angier, N. 2007. The Canon: A Whirligig Tour of the Beautiful Basics of Science. New York: First Mariner.
Baker, P. 2006. Using Corpora in Discourse Analysis. London: Continuum.
Brezina, V. 2018. Statistics in Corpus Linguistics: A practical Guide. Cambridge: Cambridge University Press.
Brezina, V., Platt, W. 2023. #LancsBox X 2.0 [software package], lancsbox.lancaster.ac.uk
McEnery, T. Xiao, R. and Yukio, T. 2006. Corpus-based Language Studies: An Advanced Resource Book. New York: Routledge.
Paquot, M. and Gries, S. (eds.) 2020. A Practical Handbook of Corpus Linguistics. Cham: Springer.
Vincent, A. 2020. The Religious Rhetoric of U.S. Presidential Candidates: A Corpus Linguistics Approach to the Rhetorical God Gap. New York: Routledge.
Angier, N. 2007. The Canon: A Whirligig Tour of the Beautiful Basics of Science. New York: First Mariner.
Baker, P. 2006. Using Corpora in Discourse Analysis. London: Continuum.
Brezina, V. 2018. Statistics in Corpus Linguistics: A practical Guide. Cambridge: Cambridge University Press.
Brezina, V., Platt, W. 2023. #LancsBox X 2.0 [software package], lancsbox.lancaster.ac.uk
McEnery, T. Xiao, R. and Yukio, T. 2006. Corpus-based Language Studies: An Advanced Resource Book. New York: Routledge.
Paquot, M. and Gries, S. (eds.) 2020. A Practical Handbook of Corpus Linguistics. Cham: Springer.
Vincent, A. 2020. The Religious Rhetoric of U.S. Presidential Candidates: A Corpus Linguistics Approach to the Rhetorical God Gap. New York: Routledge.
Faculty or entity
PHLB
