Settore: INF/01Codice: 635AACrediti: 6Semestre: 1
Docenti: Attardi Giuseppe, Esuli Andrea

Obiettivi di apprendimento


Learning fundamental techniques, algorithms and models used in natural language processing. Understanding of the architectures of typical text analytics applications and of libraries for building them. Expertise in design, implementation and evaluation of applications that exploit analysis, interpretation and transformation of texts.

Modalità di verifica delle conoscenze

Progetto o seminario.


Ability to design, implement and evaluate applications that exploit analysis, interpretation and transformation of texts.

Modalità di verifica delle capacità

Progetto o seminario.

Modalità di verifica dei comportamenti

Progetto o seminario.



Calcolo delle probabilità e statistica.


  1. Disciplinary background: Natural Language Processing, Information Retrieval and Machine Learning
  2. Mathematical background: Probability, Statistics and Algebra
  3. Linguistic essentials: words, lemmas, morphology, PoS, syntax 
  4. Basic text processing: regular expression, tokenisation
  5. Data gathering: twitter API, scraping
  6. Basic modelling: collocations, language models
  7. Introduction to Machine Learning: theory and practical tips
  8. Libraries and tools: NLTK, Keras
  9. Applications
  • Classification/Clustering
  • Sentiment Analysis/Opinion Mining
  • Information Extraction/Relation Extraction
  • Entity Linking
  • Spam Detection: mail spam & phishing, blog spam, review spam



  1. C. Manning, H. Schutze. Foundations of Statistical Natural Language Processing. MIT Press, 2000.
  2. D. Jurafsky, J.H. Martin, Speech and Language Processing. 2nd edition, Prentice-Hall, 2008.
  3. S. Kubler, R. McDonald, J. Nivre. Dependency Parsing. 2010.
  4. P. Koehn. Statistical Machine Translation. Cambridge University Press, 2010.
  5. S. Bird, E. Klein, E. Loper. Natural Language Processing with Python.
  6. I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT Press, 2016.
  7. M. Nielsen. Neural Networks and Deep Learning.

Modalità di esame



Pagina del corso


Fonte: ESSETRE e Portale esami