Natural Language Processing
A.Y. 2026/2027
Learning objectives
The aim of the course is to provide an introduction to the fundamental concepts related to Natural Language Processing (NLP) as well as an overview of the main tools used in the field. Moreover, some NLP applications will be presented, e.g. information retrieval, machine translation and automatic misogyny identification.
Expected learning outcomes
After successfully completing the course, students will be able to:
-know the base concepts of the Natural Language Processing field.
-explain the common computational vector space models for words applied in language technology.
-describe the challenges related to word vector models.
-know how to address some Natural Language Processing applications.
-know the base concepts of the Natural Language Processing field.
-explain the common computational vector space models for words applied in language technology.
-describe the challenges related to word vector models.
-know how to address some Natural Language Processing applications.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Lesson period
Second semester
Course syllabus
The course content includes fundamental principles of Natural Language Processing (NLP) and offers an overview of the key tools utilized in this field. The course will cover a range of topics, ranging from statistical techniques to recent advancements in neural approaches. Moreover, the course incorporates practical demonstrations of different NLP applications, including machine translation, and text classification.
Prerequisites for admission
Basic knowledge of statistics, machine learning and programming languages.
Teaching methods
The course will be taught in English, and it will consist of both lectures introducing the main topics and tutorial sessions.
Seminars held by experts at national and international levels may be part of the course.
Seminars held by experts at national and international levels may be part of the course.
Teaching Resources
Daniel Jurafsky and James Martin, "Speech and Language Processing, 2nd Edition", Prentice Hall, 2008.
Emily M. Bender, "Linguistic Fundamentals for Natural Language Processing", Synthesis lectures on human language technologies, Morgan&Claypool Publishers, 2013.
Yoav Goldberg, "Neural Network Methods for Natural Language Processing", Synthesis lectures on human language technologies, Morgan&Claypool Publishers, 2017.
Mohammad Taher Pilehvar and Jose Camacho-collados, "Embeddings in Natural Language Processing", Synthesis Lectures on Human Language Technologies, Morgan & Claypool Publishers, 2021.
Emily M. Bender, "Linguistic Fundamentals for Natural Language Processing", Synthesis lectures on human language technologies, Morgan&Claypool Publishers, 2013.
Yoav Goldberg, "Neural Network Methods for Natural Language Processing", Synthesis lectures on human language technologies, Morgan&Claypool Publishers, 2017.
Mohammad Taher Pilehvar and Jose Camacho-collados, "Embeddings in Natural Language Processing", Synthesis Lectures on Human Language Technologies, Morgan & Claypool Publishers, 2021.
Assessment methods and Criteria
Project
· The project consists in the development of a natural language processing tool based on methods and models presented during the course.
· Each group/individual must identify a domain of interest and dataset for which it intends to address specific NLP tasks.
· The project must be presented orally
· The project is evaluated in the range [0-24].
Oral Exam
· The oral exam can have an outcome between [-8; +8]
· It consists of 4 questions about topics addressed during the course: -2 will be given for an incorrect answer or no answer, +2 for a correct answer.
There are no mid-term tests.
· The project consists in the development of a natural language processing tool based on methods and models presented during the course.
· Each group/individual must identify a domain of interest and dataset for which it intends to address specific NLP tasks.
· The project must be presented orally
· The project is evaluated in the range [0-24].
Oral Exam
· The oral exam can have an outcome between [-8; +8]
· It consists of 4 questions about topics addressed during the course: -2 will be given for an incorrect answer or no answer, +2 for a correct answer.
There are no mid-term tests.
INFO-01/A - Informatics - University credits: 6
Lessons: 48 hours
Professor:
Raganato Alessandro