Technologies linguistiques

Teacher(s)

Pizarro Pedraza Andrea;

Language

French

Learning outcomes

At the end of this learning unit, the student is able to :

understand the basic concepts in language technologies be familiar with some of the main functions of these technologies (search, human-computer communication, machine translation, etc.) understand the fundamental techniques of Natural Language Processing be able to perform advanced queries on several platforms and corpora (e.g. Sketch Engine) build a linguistic database in Excel

Content

This course aims to introduce the basic concepts and terminology related to language technologies (generative AIs, corpora, concordancers...), in order to establish the pillars for critical and ethical use.
The theoretical explanations will be illustrated by concrete examples. In the practical part, exercises will be demonstrated on several software and platforms so that students can practice them independently.

Teaching methods

Classroom-based
- Lectures
- Practical sessions

Evaluation methods

1. Written test including theoretical questions and practical exercises (75%).
The student:

• understands the basic concepts of language technologies
• knows the important functionalities of these technologies (search, person-machine communication, automatic translation,...)
• understands the fundamental techniques of NLP

2. Exercise portfolio to be handed in on Excel (date and instructions to be confirmed at the beginning of the term). (25%)
The student:

• Is able to make advanced queries on several platforms and corpora (e.g. Sketch Engine).
• can build a linguistic database on Excel
• can test and analyse generative AIs

NB: The use of generative AIs is part of certain activities in this course. This will be clearly specified in the activity instructions. In all cases, the use of any technology will be done responsibly and in accordance with the practices of academic and scientific integrity and will therefore be systematically indicated by the student.

Online resources

Course materials on Moodle
After each topic: syllabus and instructions for the portfolio's activities

Bibliography

Supports de cours sur Moodle après chaque thème.
Références de base du syllabus :
BOUCHER Philip Nicholas (2020), Artificial intelligence: How does it work, why does it matter, and what can we do about it? https://www.europarl.europa.eu/RegData/etudes/STUD/2020/641547/EPRS_STU(2020)641547_EN.pdf
JURAFSKY,D. et J. H. MARTIN (2022): Speech and Language Processing (3ème éd.) https://web.stanford.edu/~jurafsky/slp3/
LÉON, Jacqueline. Histoire de l'automatisation des sciences du langage. Lyon: ENS Éditions, 2015 <http://books.openedition.org/enseditions/3733>. ISBN: 9782847886801. DOI: https://doi.org/10.4000/books.enseditions.3733.
MANNING, C. et SCHÜTZE, H. (1999) Foundations of Statistical Natural Language Processing, MIT Press. Cambridge, MA.
SINCLAIR, J. (2004): “Corpus and Text: Basic Principles”, in Wynne, M. (ed) Developing Linguistic Corpora: a Guide to Good Practice. Produced by AHDS, at https://users.ox.ac.uk/~martinw/dlc/chapter1.htm.

Teaching materials

• Syllabus sur Moodle après chaque thème
• Course materials on Moodle after each topic

Faculty or entity

TIMB