Multilingual natural language processing
Channel 1
ROBERTO NAVIGLI
Lecturers' profile
Program - Frequency - Exams
Course program
Introduction to Natural Language Processing
N-gram language models; smoothing; interpolation; backoff
Deep learning for NLP
Introduction to PyTorch and PyTorch Lightning
Monolingual and multilingual word embeddings, sense and concept embeddings
Neural language models: recurrent and Transformer-based (BERT, XLM-RoBERTa, GPT-x, etc.)
Large Language Models: pretraining and finetuning
Computational lexical semantics
Computational lexicons: WordNet
Multilingual semantic networks: BabelNet
Word embeddings vs. contextualized word embeddings vs. sense embeddings
Word Sense Disambiguation and Entity Linking
Multilinguality in Natural Language Processing
Computational sentence-level semantics
Neural Semantic Role Labeling and Semantic Parsing
Natural Language Generation and Question Answering
Neural Machine Translation
Prerequisites
No prerequisite.
Books
Jurafsky and Martin. Speech and Language Processing, Prentice Hall, third edition.
Frequency
In class attendance.
Exam mode
Homework submission + oral presentation.
Lesson mode
In class attendance.
ROBERTO NAVIGLI
Lecturers' profile
Program - Frequency - Exams
Course program
Introduction to Natural Language Processing
N-gram language models; smoothing; interpolation; backoff
Deep learning for NLP
Introduction to PyTorch and PyTorch Lightning
Monolingual and multilingual word embeddings, sense and concept embeddings
Neural language models: recurrent and Transformer-based (BERT, XLM-RoBERTa, GPT-x, etc.)
Large Language Models: pretraining and finetuning
Computational lexical semantics
Computational lexicons: WordNet
Multilingual semantic networks: BabelNet
Word embeddings vs. contextualized word embeddings vs. sense embeddings
Word Sense Disambiguation and Entity Linking
Multilinguality in Natural Language Processing
Computational sentence-level semantics
Neural Semantic Role Labeling and Semantic Parsing
Natural Language Generation and Question Answering
Neural Machine Translation
Prerequisites
No prerequisite.
Books
Jurafsky and Martin. Speech and Language Processing, Prentice Hall, third edition.
Frequency
In class attendance.
Exam mode
Homework submission + oral presentation.
Lesson mode
In class attendance.
- Lesson code10606869
- Academic year2025/2026
- CourseEngineering in Computer Science and Artificial Intelligence
- CurriculumSingle curriculum
- Year2nd year
- Semester2nd semester
- SSDINF/01
- CFU6