Part I - INTRODUCTION

DATE	TOPIC	MATERIALS
16/9	Natural Language Processing: Problems and perspectives	- Slides (I.1) - [FT] - Chapter 1, Section 2.1 - Papers [Abney, 2011, Aslin, 2017]
17/9	Introduction/Recall to/of probability calculus	- Slides (I.2) - [FT] - Sections 2.2, 2.2.1 - [LGS] - Sections from 1.1 to 1.2.5 - Bayesian Models [Chater et. al 2010]
17/9	N-grams Language Models	- Lesson Notes - [FT] - Sections 2.2.2, 2.2.3 - [SLP3] - Chapter 3
17/9 18/9 23/9	Introduction to Deep Learning. Transformers and Large Language Models (LLMs).	- Slide (I.3). - [FT] - Chapter 3 - [Naveed et al. 2024] - RNNs Enc/Dec + Attention [1], [2] - Transformer Explainer
23/9	The evaluation of NLP applications	- Slides (I.4) - [FT] - Chapter 5 - [OHCL] Chapter 22 - EVALITA WebSite (PoS 2007) - SemEval

REFERENCES

[FT]
Tamburini, F. (2022). Neural Models for the Automatic Processing of Italian, Bologna: Pàtron.

[SLP3]
Jurafsky and J.H. Martin (in press). Speech and Language Processing, Prentice Hall. (3rd edition DRAFT)

[LGS]
B. Krenn and C. Samuelsson (1997).The Linguist's Guide to Statistics.

[OHCL]
Mitkow R. (ed.) (2003). The Oxford Handbook of Computational Linguistics.