OVERVIEW

The Natural Language Processing course provides an introduction to the most challenging issues in processing natural languages, driven by the three layers of syntax, semantics, pragmatics; the most recent applications of natural language processing tools are discussed, including Large Language Models and the design and development of ontologies and of chatbots. 

AIMS AND CONTENT

LEARNING OUTCOMES

Learning how to process and represent natural language, and the main software components of a system able to understand natural language.

AIMS AND LEARNING OUTCOMES

At the end of the course, students will be able to use existing tools and design and implement new ones for solving Natural Language Processing problems at the syntactic and semantic level. They will also be able to design and implement a chatbot using one of the most widespread chatbot languages. Application of Machine Learning techniques to the Natural Language Processing will be analysed, as well as symbolic and knowledge based approaches.

Students interested in (and deemed suitable to, based on the outcomes on an initial quiz) attending the course in an innovative modality and in improving their soft skills, will also get the following transversal skills:
-- personal skills, basic level
-- social skills, basic level
-- creative design, advanced level

PREREQUISITES

The student should know Python

TEACHING METHODS

Frontal lessons and laboratories.
One individual project must be developed at the end of the course.

For students involved in the innovative teaching activities, the following teaching modalities will be employed:
- world café
- creative individual project (the specification of the project is invented by students and is validated by teachers

SYLLABUS/CONTENT

NLP Introduction and Terminology
Regular Expressions
Syntax at Word Level: Stop Words, TF-IDF, Stemming, Normalization, Minimum Edit Distance
Syntax at Sentence Level: Grammars, Part Of Speech (POS) Tagging with Definite Clause Grammars, POS Tagging with Hidden Markov Models, A critical comparison of DCG and HMM for POS Tagging
Semantics: Distributional semantics, word2vect, Frame Semantics, Model-theoretic semantics, Lexical Semantics, WordNet, BabelNet, Named Entity Recognition, Ontologies and the Semantic Web, Ontologies and their applications, Ontology Learning and Ontology Matching
Machine Learning and its role in solving NLP problems
Large Language Models
Pragmatics
NLP applications and recap of the Most common (non-trivial) NLP features, with examples of how and when using them
Applications: Chatbots​

RECOMMENDED READING/BIBLIOGRAPHY

The slides and the further teaching material provided during the course are enough for following it.

TEACHERS AND EXAM BOARD

Exam Board

VIVIANA MASCARDI (President)

GIOVANNA GUERRINI

GIACOMO MEANTI (President Substitute)

LESSONS

LESSONS START

In agreement with the calendar approved by the Degree Program Board of Computer Science.

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The exam will consist in a quiz with closed questions, a written part (traditional open questions, exercises) plus an individual project (requiring about 1 man/week to be completed) whose outcomes must be presented in oral form. The written part can be accessed only if the quiz has been passed.

The teachers can complement the parts above with an oral exam.

ASSESSMENT METHODS

The acquisition of the skills foreseen by this course will be assessed via the quiz and written exam + the project which have been carefully designed to allow the teachers to verify whether a student is actually able to design and implement a tool solving some (simplified) NLP problem and to understand, present and discuss in a critical way the most challenging issues raised by its development.

Exam schedule