Valutami
Scheda programma d'esame
HUMAN LANGUAGE TECHNOLOGIES
GIUSEPPE ATTARDI
Anno accademico2018/19
CdSINFORMATICA
Codice649AA
CFU9
PeriodoSecondo semestre
LinguaInglese

ModuliSettore/iTipoOreDocente/i
HUMAN LANGUAGE TECHNOLOGIESINF/01LEZIONI72
GIUSEPPE ATTARDI 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.
Pagina web del corso

https://elearning.di.unipi.it/course/view.php?id=112
Class web page

https://elearning.di.unipi.it/course/view.php?id=112
Ultimo aggiornamento 24/01/2019 00:39