Corpus linguistics: From manual to automatic analysis

Teacher(s)

de Marneffe Marie-Catherine;

Language

English
> French-friendly

Prerequisites

• A bachelor's degree  
• A solid foundation of general linguistics 
• A solid knowledge of academic English 

Main themes

This course focuses on corpus linguistics: the use of corpora in exploring theoretical questions in various areas of linguistics. The notion and specificities of a corpus of text data or oral data will be defined. Several methods (both qualitative and quantitative) that enable answering a research question based on corpus data will be introduced as well as different tools linked to these methods. The course will be hands-on: students will get practical experience with various computational tools, as well as with the statistical package R. The practical application of the concepts and methods learned will be grounded in a research project involving the full research cycle: framing of a research question, corpus construction, analyses (qualitative/quantitative), presentation of the results (both orally and written). 

Learning outcomes

At the end of this learning unit, the student is able to :
1	Build a corpus of text or oral language data for the analysis of a particular linguistic phenomena (lexical, phonetic, syntactic, semantic, or related to discourse)
2	Use several tools for corpus analysis
3	Answer a research question using quantitative corpus analysis
4	Answer a research question using qualitative corpus analysis
5	Present orally the research question, the method and results
6	Present the research question, method and results in a written academic paper
This learning unit contributes to the development and command of the following skills and learning outcomes of the ELAL programmes (ELAL learning outcomes)

Content

GENERAL OBJECTIVES :
(1) To be able to lead, completely and autonomously, a linguistic study on corpora;
(2) To acquire a general knowledge on linguistic corpora (in French, but not only), tools and methods.
SPECIFIC OBJECTIVES :
(1) To design a corpus answering a specific research question;
(2) To collect oral and/or written data + metadata;
(3) To "edit" the data (transcription, cleaning, formatting, encoding, etc.);
(4) To tag the data (at several levels of linguistic analysis) using adequate software;
(5) To formulate research questions / hypotheses;
(6) To choose a methodology of analysis;
(7) To exploit / analyze a corpus according to a chosen methology (qualitative and/or quantitative);
(8) To present the results obtained.

Teaching methods

22 hours of lectures + 10 hours of practical work (dedicated to the realization of a personal research based on the methodology see in the lectures).

Evaluation methods

The final grade is the mean of the grades obtained for the following 4 components:

• Submission of a short homework in R consisting data manipulation and vizualization (at mid-term) (20%)
• Oral presentation of their research work with visual support (20%)
• Active participation during the presentations of other students (10%)
• Written exam on the concepts from the course (50%)

For the summer (August/September) exam session, the continued evaluation will still be applicable. Students who failed one of the components will be offered the possibility to retake the component or another one judged equivalent by the professors.
Course participation
In accordance with Article 72 of the Règlement général des études et des examens (RGEE), the course instructor may propose to the examination board that a student be denied registration for the January or September exam if they have not participated regularly in learning activities and/or have failed to submit assignments within the required deadlines.

Other information

Generative artificial intelligence (AI) must be used responsibly and in accordance with academic and scientific integrity practices. Given that scientific integrity requires sources to be cited, the use of generative AI must be explicitly and thorougly acknowledged: the student is required to state (for example in a footnote) whether they used a generative AI (and which one) in writing their answers/term paper, specifying in what capacity the generative AI was used. The student remains responsible for the content of their work, regardless of the sources/references used.

This course follows the principles stated in Lignes directrices pour une utilisation des outils d’intelligence artificielles génératives en ELAL.
Whether or not it is disclosed, a potential use of generative AI tools will be considered non-compliant with academic standards when the work:

• cites bibliographic sources to support claims without having consulted the sources in question and/or without there being a close connection between these claims and the ideas developed in those sources;
• includes in the bibliography references whose validity has not been verified or that do not exist.

In case of suspected inappropriate use of generative AI, the course instructor(s) may verify compliance with the principle of authenticity of the work submitted by the student and, pursuant to Article 148 of the RGEE and with the jury's approval, request an additional assessment. Any unauthorized use of generative AI may be considered an irregularity under Article 107 of the UCLouvain RGEE.
Rules wrt plagiarism
In any academic work, students must demonstrate their full mastery of bibliographic referencing rules. To avoid suspicion of plagiarism, they must ensure to:

• not copy text without quotation marks and without citing its source;
• not reproduce graphs, data, illustrations, etc. without citing its source;
• not paraphrase or summarize an author’s original idea without citing its source;
• not translate, in full or in part, a source without referencing it;
• not use someone else’s work while presenting it as their own (even if the author of the work has given permission);
• not purchase work.

See https://www.uclouvain.be/fr/uss/lutter-contre-le-plagiat
This definition complements the one in art. 107 §2 of the RGEE and constitutes a specific provision under §3 of the same article. Students who fail to comply with the academic referencing standards outlined above may, in the event of irregularities or plagiarism identified by the examination board, face academic sanctions as detailed in the RGEE (Section 7).

Online resources

Moodle: https://moodle.uclouvain.be/course/view.php?id=10414

Bibliography

Zufferey, Sandrine (2020). Introduction to Corpus Linguistics. Wiley.

Teaching materials

• L'ensemble des supports sont disponibles sur Moodle.

Faculty or entity

> ELAL