Academic Year 2019/2020
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The student will learn the basic theoretical aspects of computational linguistics/natural language processing and will acquire practical skills to perform from tokenization and vectorization to the computation of similarities and supervised models (e.g., for topic identification, structural analysis, meaning analysis).
Whereas the contents could be (slightly) adapted according to the students skills and interests, the general structure of the course will be as follows.
Notice If you opt for turning your project into the participation to some shared task, it is alright if more than one person targets the same task.
AriEmozione: Identifying Emotions in Opera Verses
Students: Fernicola F. and Zhang S.
Developed under
CRICC
;
published in
CLiC-it 2020
[
pdf
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[
video
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UniBO@AMI: A Multi-Class Approach to Misogyny and Aggressiveness
Identification on Twitter Posts Using AlBERTo
Student: Muti, A.
Top-performing model in
Evalita’s 2020
AMI
shared task
[
pdf
]
[
video
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Identifying Characters’ Lines in Original and Translated Plays. The case of
Golden and Horan’s Class
Student: Galletti, E.
[
pdf
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Classifying An Imbalanced Dataset with CNN, RNN, and LSTM
Student: Yu, X.
[
pdf
]
Are you defending on the first/second appello? Why not turning your project into a CLIC-it paper? The deadline is on 15/07/2020.
| students | Project name | Status | Call |
|---|---|---|---|
| Alfieri, A | TBD | TBD | TBD |
| Compagnoni, A | TBD | TBD | TBD |
| Contarino, A | TBD | TBD | TBD |
| Fabbri, E | TBD | TBD | TBD |
| Fernicola, F | AriEmotion | submitted | Sep 2020 |
| Ferraiuolo, M | TBD | TBD | TBD |
| Galletti, E | Theatre’s character recognition | submitted | Feb 2021 |
| Giannoni, L | TBD | TBD | TBD |
| Guarino, E | TBD | TBD | TBD |
| Ippoliti, C | TBD | TBD | TBD |
| Martinelli, M | TBD | TBD | TBD |
| Moro, E | TBD | TBD | TBD |
| Muti, A | Evalita’s AMI (task A) | submitted | Sep |
| Norova-Lukina, V | Cognates for text intercomprehension | green flag | TBD |
| Polverino, F | TBD | TBD | TBD |
| Ravanelli, S | TBD | TBD | TBD |
| Tedesco, N | Geolocalised COVID-19 Twitter Discussion Explorer | Tentative | TBD |
| Terenzi, L | TBD | TBD | TBD |
| Vázquez C, A | TBD | TBD | TBD |
| Wang, X | TBD | TBD | TBD |
| Yu, X X (Catherine) | Focused hate-speech during the pandemia | submitted | Feb 2021 |
| Zhang, S | AriEmotion | submitted | Sep 2020 |
The course is a combination of seminar and practical sessions. In either case, active participation of the students is expected. We will start with an introduction to the Python programming language and follow with a (practical) description of diverse models and tasks.
Attendance to a minimum of 70% of the lessons is a must.
The student will work on addressing a problem within her own research interests with the knowledge acquired during the course. Upon agreement of the topic, the student will work on solving the problem and will write a written report. A poster session will be organized at the end of the course in which the students will present their research work.
The final evaluation will be computed as a combination of both report and poster presentation.
Seminars will be carried out with slides and coding will be carried out with jupyter notebooks. Continuous exercises will be carried out.
TBD