Part I - INTRODUCTION

DATE
TOPIC
MATERIALS
20/9
Natural Language Processing:
Problems and perspectives
- Slides (I.1)
- [McEW] - Chapter 1
- [SLP] - Chapter 1
- Papers [Abney, 2011, Aslin, 2017]
21/9
Introduction/Recall to/of probability calculus - Slides (I.2)
- [LGS] - Sections from 1.1 to 1.2.5
- Bayesian Models [Chater et. al 2010]
22/9
N-grams Language Models
Markov Models
- Lesson Notes
- [SLP] - Chapter 4
- Slides (I.3)
- Paper [Rabiner, 1989]
27/9 Introduction to Machine Learning - Slides (I.4).
- Paper [Emms, Luz, 2007].
28/9 Quick introduction to Deep Learning - [Bargava, 2016] Course, Slides (Complete, Extract)
29/9
Recurrent Neural Network Language Models - Paper [Mikolov, et al. 2010]
29/9 The evaluation of NLP applications - Slides (I.5)
- [OHCL] Chapter 22
- EVALITA WebSite
- SemEval
- NLP laboratory session with the Knoppix-NLP Linux Live Distribution. - Slides (I.6)
- Machine Learning laboratory session. - ML Lab link


REFERENCES

[McEW]
McEnery T., Wilson A. (1996). Corpus Linguistics, Edinburgh University Press..

[SLP]
Jurafsky and J.H. Martin (2008). Speech and Language Processing, Prentice Hall. 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.