HUMAN LANGUAGE TECHNOLOGIES
Academic year2017/18
CourseCOMPUTER SCIENCE
Code649AA
Credits9
PeriodSemester 2
LanguageItalian
Modules | Area | Type | Hours | Teacher(s) |
HUMAN LANGUAGE TECHNOLOGIES | INF/01 | LEZIONI | 72 | |
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.
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 o seminario.
Assessment criteria of knowledge
Project or seminar.
Capacità
Ability to design, implement and evaluate applications that exploit analysis, interpretation and transformation of texts.
Modalità di verifica delle capacità
Progetto o seminario.
Assessment criteria of skills
Project or seminar.
Assessment criteria of behaviors
Project or seminar.
Programma (contenuti dell'insegnamento)
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
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
Bibliografia e materiale didattico
- C. Manning, H. Schutze. Foundations of Statistical Natural Language Processing. MIT Press, 2000.
- D. Jurafsky, J.H. Martin, Speech and Language Processing. 2nd edition, Prentice-Hall, 2008.
- S. Kubler, R. McDonald, J. Nivre. Dependency Parsing. 2010.
- P. Koehn. Statistical Machine Translation. Cambridge University Press, 2010.
- S. Bird, E. Klein, E. Loper. Natural Language Processing with Python.
Bibliography
- C. Manning, H. Schutze. Foundations of Statistical Natural Language Processing. MIT Press, 2000.
- D. Jurafsky, J.H. Martin, Speech and Language Processing. 2nd edition, Prentice-Hall, 2008.
- S. Kubler, R. McDonald, J. Nivre. Dependency Parsing. 2010.
- P. Koehn. Statistical Machine Translation. Cambridge University Press, 2010.
- S. Bird, E. Klein, E. Loper. Natural Language Processing with Python.
Updated: 05/09/2017 10:52