Course Syllabus
Obiettivi
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.
Contenuti sintetici
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 information retrieval, machine translation, and automated misogyny detection.
Programma esteso
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
Prerequisiti
Basic knowledge of statistics and programming languages.
Modalità didattica
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.
Materiale didattico
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.
Periodo di erogazione dell'insegnamento
First Semester
Modalità di verifica del profitto e valutazione
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.
Orario di ricevimento
To be agreed with the teacher
Aims
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.
Contents
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 information retrieval, machine translation, and automated misogyny detection.
Detailed program
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
Prerequisites
Basic knowledge of statistics and programming languages.
Teaching form
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.
Textbook and teaching resource
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.
Semester
First Semester
Assessment method
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.
Office hours
To be agreed with the teacher