Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
| Modules | Credits | TAF | SSD |
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2° Year It will be activated in the A.Y. 2026/2027
| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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2 modules among:
- 1st year - Knowledge representation, Natural Language Processing, HCI - Multimodal Systems - delivered in 2025/2026
- 2nd year - AI & cloud - delivered in 2026/2027
- 1st and 2nd year - Advanced programming for AI, Computer vision & deep learning - delivered in 2025/2026 and in 2026/2027
2 courses among (mutually exclusive with the previous ones):
- 1st year - Knowledge representation, Natural language processing, HCI - multimodal systems - delivered in 2025/2026
- 2nd year - AI & cloud, Visual intelligence - delivered in 2026/2027
- 1st and 2nd year - Advanced programming for AI, Computer Vision & deep learning, Statistical learning - delivered in 2025/2026 and in 2026/2027 2 courses among the following
- A.A. 2025/2026 Network Science not activated
- A.A. 2026/2027: Complex Systems not activated1 course among the followingLegend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Natural Language Processing (2025/2026)
Teaching code
4S010677
Academic staff
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Period
1st semester dal Oct 1, 2025 al Jan 30, 2026.
Courses Single
Authorized
Learning objectives
The course covers the five basic areas of development of technique for the analysis of natural language: text analytics, statistical natural language processing, morpho-syntatic analysis, semantic analysis, natural logic. Specifically, the student will know segmentation techniques, stemming and lemmatization methods, POS tagging, Sentence split, logical representation of language and language-specific method for “common” morphosyntax, and more generally for those languages that have been represented with generative and categorical grammar methods. Methods based on neural networks, such as Transformers, will also be addressed in the course. Above mentioned techniques will be applied to in corpora text analysis, open and closed QA systems, machine translation and to technologies for natural language generation.
Prerequisites and basic notions
Basic notions of Logic and Machine Learning.
Program
1. Basics of text processing
1.1 Regular Expressions, Text Normalization, Edit Distance
1.2 N-gram Language Models
1.3 Naive Bayes and Sentiment Classification
2. Statistical natural language processing
2.1 Logistic Regression
2.2 Vector Semantics and Embeddings
2.3 Neural Networks and Neural Language Models
2.4 Generative Language Models and Methods based on Transformers
2.5 Sequence Labeling for Parts of Speech and Named Entities
2.6 Machine Translation
3. Symbolic methods for NLP
3.1 Constituency Grammars
3.2 Constituency Parsing
3.3 Dependency Parsing
4. Semantic technologies for NLP
4.1 Logical Representations of Sentence Meaning
4.2 Semantic technologies
4.3 Computational Semantics and Semantic Parsing
4.4 Information Extraction
4.4 Word Senses and WordNet
4.5 Semantic Role Labeling and Argument Structure
4.6 Lexicons for Sentiment, Affect, and Connotation
5. Advanced issues for text processing
5.1 Coreference Resolution
5.2 Discourse Coherence
6. Applications of NLP
6.1 Question Answering
6.2 Chatbots and Dialogue Systems
Bibliography
Didactic methods
Introductory lesson to individual topics, exercises for individual topics and review of a group of topics as scheduled in preparation for the exam.
Learning assessment procedures
To pass the exam, students must show:
- to have understood the principles underlying the functioning of automatic natural language processing
- to be able to present their arguments in a precise and organic way without digressions,
- to know how to apply the acquired knowledge to solve application problems.
Exam consists of three homework pieces and an oral optional examination.
Evaluation criteria
Correctness and completeness of the assigned works, correctness and completeness of the oral presentation.
Criteria for the composition of the final grade
The grade is determined by the weighted average of the tests in the two modules
Exam language
English
