ECTS - University of Pisa
Scheda programma d'esame
TEXT ANALYTICS
GIUSEPPE ATTARDI
Academic year2017/18
CourseBUSINESS INFORMATICS
Code635AA
Credits6
PeriodSemester 1
LanguageItalian

ModulesAreaTypeHoursTeacher(s)
TEXT ANALYTICSINF/01LEZIONI48
GIUSEPPE ATTARDI unimap
ANDREA ESULI unimap
Obiettivi di apprendimento
Conoscenze

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.
Capacità

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.
Prerequisiti (conoscenze iniziali)

Programmazione.

Calcolo delle probabilità e statistica.
Programma (contenuti dell'insegnamento)
  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

 
Bibliografia e materiale didattico
  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 d'esame

Progetto.
Pagina web del corso

http://didawiki.di.unipi.it/doku.php/mds/txa/start
Class web page

http://didawiki.di.unipi.it/doku.php/mds/txa/start
Updated: 05/09/2017 10:54