Natural Language Processing
Academic Year 2024/2025
(frontpage illustration produced with deepai’s tool in October 2024; using prompt natural language processing class for translation and technology masters).
Visit the UniBO website of the lecture for official and administrative details.
Prerequisites
A gentle introduction to Python
This topic wont be covered in class.
if you are a student of TraTec:
you had the intro to Python in PBR
elif you are a student of SpecTra:
you had the intro to python in APS
else:
check the slides, notebooks, and 2021 video recordings
Regardless, you can find the materials on virtuale.
Regardless of whether you attended either of the introductions, I suggest you to do (or re-visit) all the exercises ASAP.
Homework
Homework is going to be handled through virtuale. No further contents are expected to be shared there. On 09/10, you should have obtained the password to access from me. If you did not, ping me. Homework has associated a hard deadline.
Course contents
Whereas the contents could be (slightly) adapted according to the students skills and interests, the general structure of the course is as follows.
1. Introduction to Natural Language Processing
- MO 30/09/24 Slides
2. Words and the vector space model
- WE 02/10/24 Slides on tokens
- WE 03/10/24 Notebook on tokens and normalisation
- WE 09/10/24 Slides on VSM
- WE 09/10/24 Notebook on VSM
3. Rule-based and Naïve Bayes
- TH 10/10/24 Slides on RB sentiment (+ naive bayes)
- TH 10/10/24 Notebook on RB sentiment
- WE 16/10/24 Notebook on Naïve Bayes
4. Word vectors
- TH 17/10/24 Slides on tf-idf
TH 17/10/24WE 23/10/24 Notebook
5. From Word Counts to Meaning
WE 23/10/24TH 24/10/24 Slides introducing topic modellingWE 23/10/23TH 24/10/24 Notebook on topic modelling
6. Training and Evaluation
- 30/10/23 Slides on training and evaluation
- 30/10/23 Notebook
7. Intro to NN
Intermezzo
8. Word Embeddings
9. Doc2Vec
10. Convolutions for text
11. Text is Sequential / LSTM
12. Text generation
13. Intro to Seq2Seq and Transformers ; Closing Remaks
13. A brief intro to LLMs
Calendars
Table 1 shows the calendar for the 20 NLP lessons.
This year, NLP has two sibling lessons:
- Selected Topics in Natural Language Processing is an optional (with credits). Further information about it is available on the UniBO website. Table 2 shows the calendar of the 8 lessons.
- Tutorato of NLP is made to support you in the programming side of NLP. Table 3 shows the calendar of the 10 lessons.
| Lesson | Date | Time | Location | Lesson | Date | Time | Location |
|---|---|---|---|---|---|---|---|
| 1 | MO 30 Sep | 15:15 | lab 10 | 11 | WE 13 Nov | 13:30 | lab 16 |
| 2 | WE 2 Oct | 13:30 | lab 16 | 12 | TH 14 Nov | 13:30 | lab 16 |
| 3 | WE 9 Oct | 13:30 | lab 16 | 13 | MO 18 Nov | 15:15 | lab 10 |
| 4 | TH 10 Oct | 13:30 | lab 10 | 14 | WE 20 Nov | 13:30 | lab 16 |
| 5 | WE 16 Oct | 13:30 | lab 16 | 15 | WE 27 Nov | 13:30 | lab 16 |
| 6 | TH 17 Oct | 13:30 | lab 10 | 16 | TH 28 Nov | 13:30 | lab 10 |
| 7 | WE 23 Oct | 13:30 | lab 16 | 17 | MO 2 Dec | 15:15 | lab 10 |
| 8 | TH 24 Oct | 13:30 | lab 10 | 18 | MO 9 Dec | 15:15 | lab 10 |
| 9 | WE 30 Oct | 13:30 | lab 16 | 19 | WE 11 Dec | 13:30 | lab 16 |
| 10 | TH 31 Oct | 13:30 | lab 10 | 20 | MO 16 Dec | 15:15 | lab 10 |
| Lesson | Date | Time | Location | Lesson | Date | Time | Location |
|---|---|---|---|---|---|---|---|
| 1 | WE 9 Oct | 17:00 | lab 9 | 5 | WE 13 Nov | 17:00 | lab 9 |
| 2 | WE 16 Oct | 17:00 | lab 9 | 6 | WE 20 Nov(1) | 17:00 | lab 9 |
| 3 | WE 23 Oct | 17:00 | lab 9 | 7 | WE 27 Nov | 17:00 | lab 9 |
| 4 | WE 30 Oct | 17:00 | lab 9 | 8 | WE 15 Dec | 17:00 | lab 9 |
(1) ImprenDITori di se stessi. TH aula 10
| Lesson | Date | Time | Location | Lesson | Date | Time | Location |
|---|---|---|---|---|---|---|---|
| 1 | TH 3 Oct | 17:00 | lab 10 | 6 | TH 7 Nov | 17:00 | lab 10 |
| 2 | TH 10 Oct | 17:00 | lab 10 | 7 | TH 14 Nov | 17:00 | lab 10 |
| 3 | TH 17 Oct | 17:00 | lab 10 | 8 | TH 21 Nov | 17:00 | lab 10 |
| 4 | TH 24 Oct | 17:00 | lab 10 | 9 | TH 28 Nov | 17:00 | lab 10 |
| 5 | TH 31 Oct | 17:00 | lab 10 | 10 | TH 5 Dec | 17:00 | lab 10 |
Projects
For your final mark, 80% comes from the final project. Look for inspiration, in the projects presented in previous years
Some project ideas
Eventually, I will drop here some ideas for final projects.
Previous final projects
2024-2025
yours will be here
2023-2024
- Cupin E., Galiero L., and Ciminari D. (2023).
Back to the Roots: Tracing Source Languages in Wikipedia with LABSE
🗎
2022-2023
- Mainardi. P (2023).
Identifying masculine generics in Italian
🗎
2021-2022
Gajo, P. (2022). Hate Speech Detection in Incel Online Spaces
🗎Kovacs, M. (2022). Fishing for catfishes: using a model trained on Twitter data to predict author gender in Reddit posts
🗎
2020-2021
- Hopkins, D. (2022). Assessing Semantic Similarity between Original Texts and Machine Translations
🗎
Galletti, E. (2021). Identifying Characters’ Lines in Original and Translated Plays. The case of Golden and Horan’s Class
🗎Yu, X. (2021). Classifying An Imbalanced Dataset with CNN, RNN, and LSTM
🗎
2019-2020
Fernicola F. and Zhang S. (2020). AriEmozione: Identifying Emotions in Opera Verses
(developed under CRICC; published in CLiC-it 2020)
🗎 🎦Muti, A. (2020). UniBO@AMI: A Multi-Class Approach to Misogyny and Aggressiveness Identification on Twitter Posts Using AlBERTo
(top-performing model in Evalita’s 2020 AMI shared task)
🗎 🎦