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.

Type D and Type F activities

This information is intended exclusively for students already enrolled in this course.
If you are a new student interested in enrolling, you can find information about the course of study on the course page:

Laurea magistrale in Artificial Intelligence - Enrollment from 2025/2026

Type D learning activities are the student's choice, type F activities are additional knowledge useful for job placement (internships, transversal skills, project works, etc.). According to the Teaching Regulations of the Course, some activities can be chosen and entered independently in the booklet, others must be approved by a special committee to verify their consistency with the study plan. Type D or F learning activities can be covered by the following activities.

1. Modules taught at the University of Verona

Include the modules listed below and/or in the Course Catalogue (which can also be filtered by language of delivery via Advanced Search).

Booklet entry mode: if the teaching is included among those listed below, the student can enter it independently during the period in which the curriculum is open; otherwise, the student must make a request to the Secretariat, sending the form to carriere.scienze@ateneo.univr.it during the period indicated.

2. CLA certificate or language equivalency

In addition to those required by the curriculum/study plan, the following are recognized for those matriculated from A.Y. 2021/2022:

  • English language: 3 CFUs are recognized for each level of proficiency above that required by the course of study (if not already recognized in the previous course of study).
  • Other languages and Italian for foreigners: 3 CFUs are recognized for each proficiency level starting from A2 (if not already recognized in the previous study cycle).

These CFUs will be recognized, up to a maximum of 6 CFUs in total, of type F if the study plan allows it, or of type D. Additional elective credits for language knowledge may be recognized only if consistent with the student's educational project and if adequately justified.

Those enrolled until A.Y. 2020/2021 should consult the information found here.

Method of inclusion in the bookletrequest the certificate or equivalency from CLA and send it to the Student Secretariat - Careers for the inclusion of the exam in the career, by email: carriere.scienze@ateneo.univr.it

3. Transversal skills

Discover the training paths promoted by the University's TALC - Teaching and learning center intended for students regularly enrolled in the academic year of course delivery https://talc.univr.it/it/competenze-trasversali

Mode of inclusion in the booklet: the teaching is not expected to be included in the curriculum. Only upon obtaining the Open Badge will the booklet CFUs be automatically validated. The registration of CFUs in career is not instantaneous, but there will be some technical time to wait.  

4. Contamination lab

The Contamination Lab Verona (CLab Verona) is an experiential course with modules on innovation and enterprise culture that offers the opportunity to work in teams with students from all areas to solve challenges set by companies and organisations.  

Upon completion of a CLab, students will be entitled to receive 6 CFU (D- or F-type credits).  

Find out more:  https://www.univr.it/clabverona 

PLEASE NOTE: In order to be admitted to any teaching activities, including those of your choice, you must be enrolled in the academic year in which the activities in question are offered. Students who are about to graduate in the December and April sessions are therefore advised NOT to undertake extracurricular activities in the new academic year in which they are not enrolled, as these graduation sessions are valid for students enrolled in the previous academic year. Therefore, students who undertake an activity in an academic year in which they are not enrolled will not be granted CFU credits.  

5. Internship/internship period

In addition to the CFUs stipulated in the curriculum/study plan (check carefully what is indicated on the Teaching Regulationshere you can find information on how to activate the internship. 

Check in the regulations which activities can be Type D and which can be Type F.

Please also note that for traineeships activated after 1 October 2024, it will be possible to recognise excess hours in terms of type D credits limited only to traineeship experiences carried out at host organisations outside the University.

Academic year:

Teaching code

4S010677

Coordinator

Matteo Cristani

Credits

6

Language

English en

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Period

Semester 1  dal Oct 1, 2024 al Jan 31, 2025.

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 Sequence Labeling for Parts of Speech and Named Entities
2.5 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

Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

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.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

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 final grade is formed through the sum of the three tests, each evaluated in tenths, to which is added a score from 0 to 5 which evaluates the oral test, up to saturation. The score of 30 with honors can only be obtained if the three tests assigned at home are rated 10 each and the oral test is worth at least 3.

Exam language

English