B5685 - Selected Topics in Natural Language Processing

Learning outcomes

The student knows the main formats for data annotation, and the main strategies to source annotation from experts and/or crowd-sourcing platforms; s/he is able define a complex problem in natural language processing settings, to identify the appropriate data that has to be compiled in order to address it and to implement solutions that go beyond supervised ones; s/he is also familiar with relevant topics in NLP and artificial intelligence in general, including ethical aspects and process upscaling.

Course contents

This lesson is composed of two parts. The first one intends to prepare the student to face the problem of producing a (supervised) dataset from scratch in order to have materials to learn a model.The second, more substantial, is about natural language processing built with neural networks.

Part 1. Datasets for NLP

1. Definition of the problem, annotation scheme and guidelines.
2. Annotation by experts and by crowdsourcing.
3. Ethical aspects of tasks, annotation and crowdsourcing

Part 2. Neural networks and NLP

1. Sequence to sequence models
2. Encoding transformers
3. Decoding transformers (LLMs)
4. Fine-tuning and prompting

Readings/Bibliography

1. Yoav Goldberg. (2017). Neural Network Methods for Natural Language Processing (G. Hirst, ed.). Morgan & Claypool Publishers.
2. Dan Jurafsky and James H. Martin. Speech and Language Processing (3rd ed. draft) Draft chapters in progress, January 6, 2026
3. Simon J.D. Prince (2023). Understanding Deep Learning. The MIT Press.
4. Sinclair, J. 2005. Developing linguistic corpora: a guide to good practice. Chapter 1: Corpus and Text — Basic Principles. AHDS Literature, Language, and Lingustics.

 

More topic-specific materials will be provided over the semester.

Teaching methods

The course will be a combination of seminar and practical sessions. In either case, active participation of the students is expected.

Assessment methods

• 100% Final project + oral exam

Regarding the final project, the student will work on addressing a problem within his/her own research interests with the knowledge acquired during the course. Upon agreement of the topic with the instructor, the student will work on solving the problem (program) and will write a written report.

With regard to assessment, limited use of AI is permitted for specific tasks, such as spelling and grammar check. The student will be asked to submit a statement indicating how AI was used. Generative use of AI to actually produce
exam work is strictly forbidden. Any violation of this rule will be considered a serious breach of academic ethics.

 

Grading scale

• 30-30L: The student possesses an in-depth knowledge of the topic, an outstanding ability to apply the concepts. The student carries out rigorous formal experiments and produces an outstanding report, enough to be considered for submission to a national conference in the field.
• 27–29: The student possesses an in-depth knowledge of the topic, a sound ability to apply concepts, and good analytical skills. The student carries out good formal experiments and produces a high-quality report.
• 24-26: The candidate possesses a fair knowledge of the topic and a reasonable ability to apply concepts correctly. The student carries out some reasonable experiments and produces a good report.
• 21-23: The candidate possesses an adequate, but not in-depth, knowledge of the topic and a partial ability to apply concepts. The student carries out faulty experiments and produces a reasonable report.
• 18-20: The candidate possesses a barely adequate and only superficial knowledge of topic and only an inconsistent ability to apply concepts. The student carries out wrong experiments and produces a defficient report.
• < 18 Fail: The candidate possesses an inadequate knowledge of the topic, makes significant errors in applying concepts. Both experiments and report are poor.

Students with specific learning difficulties (SpLD) or with disabilities that can affect their ability to attend courses are invited to contact the University service for students with disabilities and SLD [https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students] at the earliest opportunity -- ideally before the start of the course. The University service will suggest possible adjustments to the course work and/or exam, which must then be submitted to the course leader so they can assess their feasibility, in line with the learning objectives of the course. Please note that adjustments to the exam must be requested at least two weeks in advance.

Teaching tools

Seminars will be lectured with slides and coding will be produced on jupyter notebooks. Continuous exercises will be carried out.

Office hours

See the website of Luis Alberto Barron Cedeno