Natural Language Processing
A.Y. 2023/2024
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: First 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
First semester
Course syllabus
Introduction to data pre-processing and to some NLP tasks, such as part of speech tagging, and named entity recognition.
Text representation (eg. tf-idf)
Statistical LM (eg. n-gram model)
Dense vector representation (eg. Word2Vec, FastText, etc.)
Dense contextualized word vectors (eg. Neural Language Model)
Sequence2sequence models for NLP (eg. Encoder-Decoder)
Applications of NLP:
Information Retrieval
Automatic Misogyny Identification
Machine Translation
Text representation (eg. tf-idf)
Statistical LM (eg. n-gram model)
Dense vector representation (eg. Word2Vec, FastText, etc.)
Dense contextualized word vectors (eg. Neural Language Model)
Sequence2sequence models for NLP (eg. Encoder-Decoder)
Applications of NLP:
Information Retrieval
Automatic Misogyny Identification
Machine Translation
Prerequisites for admission
Basic knowledge of statistics 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 where open-source tools will be explained. 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
Written and optional oral individual examination.
The written examination is aimed at assessing the level of understanding of the basic aspects taught during the course; it is constituted by a set of open questions.
The written examination is aimed at assessing the level of understanding of the basic aspects taught during the course; it is constituted by a set of open questions.
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours
Professors:
Fersini Elisabetta, Pasi Gabriella, Raganato Alessandro
Educational website(s)