Scheda programma d'esame
TEXT ANALYTICS
GIUSEPPE ATTARDI
Anno accademico2018/19
CdSDATA SCIENCE AND BUSINESS INFORMATICS
Codice635AA
CFU6
PeriodoPrimo semestre
LinguaItaliano

ModuliSettoreTipoOreDocente/i
TEXT ANALYTICSINF/01LEZIONI48
GIUSEPPE ATTARDI unimap
ANDREA ESULI unimap
Obiettivi di apprendimento
Learning outcomes
Knowledge

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

Assessment criteria of knowledge

Homeworks and final project.

Skills

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

Syllabus

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
Bibliography
  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.
Ultimo aggiornamento 24/01/2019 00:39