PRESENTAZIONE

L'insegnamento Natural Language Processing fornisce un'introduzione ai problemi più sfidanti nell'elaborazione dei linguaggi naturali, considerando i tre livelli di sintassi, semantica, pragmatica; vengono discusse le più recenti applicazioni degli strumenti di elaborazione del linguaggio naturale, compresa la progettazione e lo sviluppo di ontologie e di chatbot. 

OBIETTIVI E CONTENUTI

OBIETTIVI FORMATIVI

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

OBIETTIVI FORMATIVI (DETTAGLIO) E RISULTATI DI APPRENDIMENTO

After 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

PREREQUISITI

The student should know Python

MODALITA' DIDATTICHE

Frontal lessons (24 hours) and laboratories (8 hours).

One individual project on a topic proposed by the teacher 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)

PROGRAMMA/CONTENUTO

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
Pragmatics
NLP applications and recap of the Most common (non-trivial) NLP features, with examples of how and when using them
Applications: Chatbots​

TESTI/BIBLIOGRAFIA

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

DOCENTI E COMMISSIONI

Commissione d'esame

VIVIANA MASCARDI (Presidente)

GIOVANNA GUERRINI

GIACOMO MEANTI (Presidente Supplente)

LEZIONI

INIZIO LEZIONI

In accordance to the academic calendar of the Computer Science Degree

Orari delle lezioni

L'orario di questo insegnamento è consultabile all'indirizzo: Portale EasyAcademy

ESAMI

MODALITA' D'ESAME

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.

MODALITA' DI ACCERTAMENTO

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.