The aim of the course is to provide an introduction to the fundamental concepts related to the Linguistic aspects of human languages, and Natural Language Processing (NLP) techniques; moreover, in the course, some NLP applications will be presented, i.e. Information Retrieval and Machine Translation.
After successfully completing the course, students will be able to:
-describe basic linguistic aspects of human languages. -explain the common computational vector space models for words applied in language technology. -describe the challenges related to word vector models. -know the main neural language models and apply them for different applications.
Prerequisiti
Basic knowledge of statistics, programming languages, and machine learning.
Metodi didattici
The course will be constituted of both lectures introducing the main topics and laboratory sessions where open source tools will be explained and employed. Seminars held by experts at national and international levels will be part of the course.
Verifica Apprendimento
Written and optional oral individual examination, definition of a laboratory project that can be developed also by groups of students (up to three students).
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 goal of the group project is the usage of open-source software that will be employed to develop technological solutions to the problems addressed in the course. In particular, real application areas will be considered, which require the definition of systems presented during the course.
Testi
Emily M. Bender, "Linguistic Fundamentals for Natural Language Processing", Synthesis lectures on human language technologies, Morgan&Claypool Publishers, 2013.
Daniel Jurafsky and James Martin, "Speech and Language Processing, 2nd Edition", Prentice Hall, 2008.
Yoav Goldberg, "Neural Network Methods for Natural Language Processing", Synthesis lectures on human language technologies, Morgan&Claypool Publishers, 2017.
Contenuti
This course will first provide the notions of the morphological and syntactic structure of human languages, useful in creating more linguistically aware NLP systems.
The course will then introduce some NLP tasks and text representation techniques. Starting from statistical methods to modern neural approaches, an overview of fundamental techniques will be presented and practiced, such as the n-gram model, Word2Vec, the encoder-decoder paradigm, and neural language models. Open-source software for NLP will be introduced and used throughout the lab sessions.
