• programming skills, proficiency in the programming language Python
• elementary Calculus and Linear Algebra (e.g. course “Computational Mathematics for learning and data analysis” (646AA))
• elements of probability and statistics (e.g. course “Calcolo delle Probabilità e Statistica” (269AA))
• machine learning (e.g. course “Machine Learning” (654AA))

The course presents principles, models and the state of the art techniques for the analysis of natural language, focusing mainly on statistical machine learning approaches and Deep Learning in particular. Students will learn how to apply these techniques in a wide range of applications using modern programming libraries.Formal and statistical approaches to NLP.

• Statistical methods: Language Model, Hidden Markov Model, Viterbi Algorithm, Generative vs Discriminative Models
• Linguistic essentials: words, lemmas, morphology, PoS, phrases.
• Parsing: constituency and dependency parsing.
• Processing Pipelines: UIMA, Tanl
• Lexical semantics: collocations, corpora, thesauri, gazetteers.
• Distributional Semantics: Word embeddings, Character embeddings.
• Deep Learning for natural language.
• Applications: Entity recognition, Entity linking, Classification, Summarization.
• Opinion mining, Sentiment Analysis.
• Question answering, Language inference, Dialogic interfaces (chatbots)
• Statistical Machine Translation.
• NLP libraries: NLTK, Theano, Tensorflow, Keras

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.

Final project and discussion.