- Docente: Fabio Tamburini
- Credits: 6
- SSD: L-LIN/01
- Language: Italian
- Teaching Mode: Traditional lectures
- Campus: Bologna
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Corso:
Second cycle degree programme (LM) in
Data, Methods and Theoretical Models For Linguistics (cod. 5946)
Also valid for Second cycle degree programme (LM) in Italian Studies, European Literary Cultures, Linguistics (cod. 9220)
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from Mar 18, 2024 to May 10, 2024
Learning outcomes
At the end of the course the student will know the advanced techniques for the management of linguistic corpora as well as basic notions of statistical analysis on linguistic data.
Course contents
NLP: Applicazioni & Case Study
- Solving downstream tasks with Large Language Models
- Prompting Pre-Trained Language Models
- Identificazione Automatica della Prominenza Prosodica
- Cenni di Stilometria e Dialettometria.
Machine Learning Laboratory
Statistical analysis of linguistic data.
- On the importance of quantitative analysis for linguistics.
- Fundamentals of statistic package R.
- Descriptive statistics.
- Analytical/Inferential statistics.
Readings/Bibliography
Some chapters extracted from:
- Tamburini F. (2022). Neural Models for the Automatic Processing of Italian, Bologna: Pàtron.
- Gries, S. (2009). Statistics for Linguistics with R. De Gruyter.
Slides, handouts and papers downloadable from the course web site https://corpora.ficlit.unibo.it/TAL/ .
The course contents for students not attending the lessons are the same. However, students not able to attend the lessons are strongly invited to contact the teacher to get some explanations and avoid any misunderstanding about the course contents and reading materials.
Teaching methods
Face-to-face classes and laboratory sessions for 30 hours.
Assessment methods
The student has to solve three exercises given by the teacher and has to produce a report showing the proposed solutions. The exam consists of an oral colloquium on the course contents and on the student report designed to evaluate the critical skills and methodological knowledge gained by the student.
Reaching a clear view of all the course topics as well as using a correct language terminology will be valued with maximum rankings.
Mnemonic knowledge of the course topics or not completely appropriate terminology will be valued with intermediate rankings.
Unknown topics or inappropriate terminology use will be valued, depending on the seriousness of the omissions, with minimal or insufficient rankings.
It is compulsory to register for the exam using the online [https://almaesami.unibo.it/almaesami/welcome.htm] procedure.
Students with disabilities and Specific Learning Disorders (SLD)
Students with disabilities or Specific Learning Disorders have the right to special accommodations according to their condition, following an assessment by the Service for Students with Disabilities and SLD. Please do not contact the teacher but get in touch with the Service directly to schedule an appointment. It will be the responsibility of the Service to determine the appropriate adaptations. For more information, visit the page:
https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students
Teaching tools
The course web site is the central point for any kind of information about the course. It contains the handouts and the readings discussed during the lessons as well as a rich software repository useful for laboratory practice.
A USB key downloadable from the course website has been prepared for the students containing a complete computing environment to practice with the procedures proposed during the course.
Links to further information
https://corpora.ficlit.unibo.it/TAL/
Office hours
See the website of Fabio Tamburini
SDGs
This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.