91258 - Natural Language Processing

Learning outcomes

At the end of the course, the student masters the basic algorithms, tools and systems for the management, processing and analysis of natural language. The student is able to design and develop simple systems oriented to real-world natural language processing applications such as those requiring segmentation and classification of elements of text.

Course contents

Topics

• Introduction to NLP
• Elements of computational linguistics
• Basic text processing
• Language modeling with n-grams
• Text classification using linear models
  
• Vector semantics and embeddings
• Neural language modeling
  
• RNNs for sequence processing
• Contextual word embeddings, attention and transformers
• Large language models
• Basics of information retrieval
• Elements of speech processing
  
• NLP libraries and tools
  
• NLP applications

Prerequisites

The course is intended for 2nd-year students of the Masters' Degree in Artificial Intelligence, who have a solid background in foundational subjects and technological tools for tackling modern Natural Language Processing. There are no formal prerequisites in terms of exams taken, but students are expected to be familiar with machine learning and deep learning concepts, methodologies and tools, fundamentals of AI & KR, statistical and mathematical methods for AI, and the Python programming language.

Readings/Bibliography

• Speech and Language Processing, by Dan Jurafsky and James H. Martin. 3rd Ed. draft
• Other resources and scientific articles that will be made available throughout the course.
  

Teaching methods

The course combines theoretical foundations with practical modeling and solving of realistic problems. The main teaching methods are lectures and exercises, hands-on tutorials, practical assignments, and seminars by experts on selected topics.

Assessment methods

The course will include two graded assignments, to be solved in groups of 3/4 students, and a closed-book written exam, to be sat individually after submitting the assignments. 

The written exam is intended to assess the degree of understanding of the course topics. Assignments are intended to assess the student's ability to solve a small-scale NLP task. Access to the written exam is possible only after submitting the assignments by the relevant cut-off date. There will be three cut-off dates:

• 13 January 2025
  
• 31 May 2025
• 31 August 2025

The last cut-off date is also the deadline for submitting assignments for the 2024/25 academic year. Once submitted, assignments are valid for the whole academic year and grant access to all subsequent written exam sessions until the end of 2025.

Each assignment is graded in a 0-6 scale, based on the timeliness, methodological soundness, quality and documentation of the submitted solution. Written exams are graded in a 0-18 scale, based on the correctness and comprehensiveness of the answers. Solutions to assignments or written exams that demonstrate an outstanding mastery of the course topics are awarded 0.5 extra points.

Assignments submitted by the first cut-off date (15 January 2025) are awarded 0.5 bonus points.

The final grade is the sum of all the grades obtained with assignments and written exam. Half points are rounded up until 30. After 30 there is no rounding up. "30 cum laude" requires reaching 31 points.

Teaching tools

Relevant learning material including slides and notebooks will be made available via virtuale. All software resources required for this course are open-source and easy to obtain on the Internet.

Links to further information

https://site.unibo.it/nlp/en

Office hours

See the website of Paolo Torroni