Scheda insegnamento

  • Docente Fabio Tamburini

  • Crediti formativi 6

  • SSD INF/01

  • Modalità di erogazione In presenza (Convenzionale)

  • Lingua di insegnamento Inglese

  • Orario delle lezioni dal 13/11/2017 al 15/12/2017

Anno Accademico 2017/2018

Conoscenze e abilità da conseguire

Al termine del corso, lo studente conosce le principali problematiche relative al riconoscimento del linguaggio naturale e alla sua implementazione. È in grado di realizzare analizzatori sintattici di parti semplici del linguaggio naturale.


  • Part I: Foundations
    • Introduction
      • Natural Language Processing - Problems and perspectives
      • Introduction to probability calculus
      • N-grams and Language Models, Markov Models
      • Introduction to Machine Learning
      • The evaluation of NLP applications
    • Corpora
      • Corpus construction: representativeness
      • Concordances, collocations and measure of words association
      • Methods for Text Retrieval
      • Regular Expressions
  • Part II: Natural Language Processing
    • Tokenisation and Sentence splitting
    • Computational Phonetics
      • Speech samples: properties and acoustic measures
      • Analysis in the frequency domain, Spectrograms
      • Applications in the acoustic phonetic field.
    • Computational Morphology
      • Morphological operations
      • Static lexica, Two-level morphology
    • Computational Syntax
      • Part-of-speech tagging
      • Grammars for natural language
      • Natural language Parsing
      • Supplementary worksheet: formal grammars for NL
        • Formal languages and Natural languages. Natural language complexity
        • Phrase structure grammars, Dependency Grammars
        • Treebanks
        • Modern formalisms for parsing natural languages
    • Computational Semantics
      • Lexical semantics: WordNet and FrameNet
      • Word Sense Disambiguation
      • Word-Space models
      • Logical approaches to sentence semantics
  • Part III: Applications and Case studies:
    • Emotions and Sentiment in Speech and language
    • Topic modelling
    • (Automatic detection of Prosodic Prominence)
    • (Stylometry and Dialectometrics)



Some chapters extracted from:

  • McEnery T., Wilson A. (1996). Corpus Linguistics, Edinburgh University Press.
  • D. Jurafsky and J.H. Martin (2008). Speech and Language Processing, Prentice Hall. 
  • A. Clark, C. Fox, S. Lappin (2010). The Handbook of Computational Linguistics and Natural Language Processing, Blackwell Handbooks in Linguistics.
  • Mitkow R. (ed.) (2003). The Oxford Handbook of Computational Linguistics.
  • Ritchie C. and Mellish C. (2000). Techniques in Natural Language Processing.

Slides, handouts and papers downloadable from the course web site.


Metodi didattici

Face-to-face classes and labs for 30 hours.


Modalità di verifica dell'apprendimento

The exam consists of an oral colloquium on the course contents designed to evaluate the critical skills and methodological knowledge gained by the student.

Reaching a clear view of all the course topics as well as using a correct language terminology will be valued with maximum rankings.
Mnemonic knowledge of the course topics or not completely appropriate terminology will be valued with intermediate rankings.
Unknown topics or inappropriate terminology use will be valued, depending on the seriousness of the omissions, with minimal or insufficient rankings.

It is compulsory to register for the exam using the online procedure.


Strumenti a supporto della didattica

The course web site is the central point for any kind of information about the course. It contains the handouts and the readings discussed during the lessons as well as a rich software repository useful for laboratory practice.

A CD-ROM/USB key has been prepared for the students containing a complete computing environment to practice with the procedures proposed during the course. This tool will be used also in the laboratory sessions.


Orario di ricevimento

Consulta il sito web di Fabio Tamburini