HUMAN LANGUAGE TECHNOLOGIES
Academic year2020/21
CourseCOMPUTER SCIENCE
Code649AA
Credits9
PeriodSemester 2
LanguageEnglish
| 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
Homeworks and final project.
Assessment criteria of knowledge
Homeworks and final project.
Capacità
Ability to design, implement and evaluate applications that exploit analysis, interpretation and transformation of texts.
Skills
Ability to design, implement and evaluate applications that exploit analysis, interpretation and transformation of texts.
Prerequisiti (conoscenze iniziali)
- programming skills, proficiency in the programming language Python
- elementary Calculus and Linear Algebra (e.g. course “Computational Mathematics for learning and data analysis” (646AA))
- elements of probability and statistics (e.g. course “Calcolo delle Probabilità e Statistica” (269AA))
- machine learning (e.g. course “Machine Learning” (654AA))
Prerequisites
- programming skills, proficiency in the programming language Python
- elementary Calculus and Linear Algebra (e.g. course “Computational Mathematics for learning and data analysis” (646AA))
- elements of probability and statistics (e.g. course “Calcolo delle Probabilità e Statistica” (269AA))
- machine learning (e.g. course “Machine Learning” (654AA))
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.
Modalità d'esame
Project.
Assessment methods
Final project and discussion.
Updated: 27/09/2020 19:27
