Information Processing and Retrieval

Code:

M.IA031

Acronym:

PRI

Keywords
Classification	Keyword
OFICIAL	Computer Science
OFICIAL	Informatics Engineering

Instance: 2025/2026 - 1S

Active?	Yes
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Master in Artificial Intelligence

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M.IA	0	Syllabus	1	-	6	42	162
			2

Teaching language

English

Objectives

The course Information Processing and Retrieval (PRI) aims to prepare students to understand, design, and develop effective solutions for information processing and retrieval.

The specific objectives are:

1. To raise students’ awareness of the main challenges involved in building information retrieval systems;
2. To familiarize students with the key concepts and techniques of information processing and retrieval;
3. To enable students to design, implement, and evaluate information retrieval systems applied to different types of document collections.

Learning outcomes and competences

Upon completing this course unit, students should be able to design and implement an information processing and retrieval system.

In particular, students should be able to:

• Identify and describe the main tasks associated with information processing and retrieval;
• Explain the architecture and functioning of an information retrieval system;
• Describe the tasks involved in the processing of a collection and in the processing of queries;
• Distinguish between different information retrieval models, identifying their principles, document representation models, and similarity measures;
• Describe and implement different techniques for information indexing;
• Describe and implement different techniques for information retrieval and ranking of results.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Programming: knowledge and practice with programming languages ​​for application development.

Databases: knowledge and practice of data modeling using UML.

Program

Information Processing

• Fundamental concepts in data-intensive systems;
• Data collection, ingestion, preparation, and integration;
• NLP techniques for preprocessing and enrichment;
• Patterns and tools for data pipelines;
• Scalability, distribution, and operation in modern environments.

Fundamentals of Information Retrieval

• Information retrieval versus data retrieval;
• Evolution and development of the field;
• Main retrieval tasks and processes.

Architecture of Retrieval Systems

• Components of a search system;
• Acquisition and preparation of information for indexing;
• Text processing: lexical analysis, morphological normalization, compression;
• Indexing: inverted indexes, construction, and access;
• Query processing, interaction, ranking, and evaluation of results.

Retrieval Models

• Classical models: Boolean, vector space, probabilistic;
• Dense and neural models: embeddings, contextual representations;
• Hybrid retrieval (sparse + dense);
• Ranking algorithms and learning to rank.

Retrieval in Modern Environments

• Web search engines: PageRank and HITS;
• Retrieval in distributed and scalable environments;
• Multimodal retrieval (text, image, audio, video);
• Integration with large language models (retrieval-augmented generation).

Evaluation of Information Retrieval Systems

• Test collections, topics, and relevance assessments;
• Evaluation metrics: precision, recall, MAP, nDCG;
• Evaluation based on user interactions (click logs, implicit feedback);
• Issues of robustness, fairness, and ethics.

Mandatory literature

Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze; Introduction to Information Retrieval, Cambridge University Press, 2008. ISBN: 0521865719
W. Bruce Croft; Search engines. ISBN: 978-0-13-136489-9
Ricardo Baeza-Yates; Modern information retrieval. ISBN: 978-0321416919
Marti Hearst; Search User Interfaces, Cambridge University Press, 2009

Teaching methods and learning activities

The program topics are addressed in lecture-practical sessions, combining theoretical exposition and laboratory work. Each student group defines and develops a project throughout the semester, with part of the development, supervision, and assessment taking place during class.

Project: design and implementation of an information processing and retrieval system, developed in student groups. The project is structured into partial deliverables and presentations, corresponding to the different phases of development.

The project development is supervised during the lecture-practical sessions and assessed based on continuous monitoring, submitted reports, and presentations.

Theoretical knowledge is assessed through a final exam, consisting of multiple-choice and open-ended questions.

Software

OpenRefine
Apache Lucene
Apache Solr
Docker

keywords

Physical sciences > Computer science > Informatics

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	40,00
Trabalho prático ou de projeto	60,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de projeto	70,00
Estudo autónomo	50,00
Frequência das aulas	42,00
Total:	162,00

Eligibility for exams

The conditions for obtaining attendance are as follows:

1. Not exceeding the maximum number of absences from practical classes (25% of the total number of practical sessions in the semester);
2. Registering in a project group within the defined period;
3. Participating in all phases of project development;
4. Participating in the final presentation and defense of the project.

Participation in the project development (point 3) is assessed based on the evidence produced by the student (code and documentation), the instructors’ evaluation during practical classes, and the self-assessment and peer assessment carried out within the group.

Calculation formula of final grade

The final grade is calculated according to the following formula:

NF = 60% Project + 40% Exam

Approval in the project requires the participation of each student in all phases of development, namely in the selection of data sources, the choice of technologies, the identification and characterization of the problem, the design and implementation of the solution, the writing of the reports, and the project presentations.

The individual final grade for the project may vary among members of the same group, by up to 3 points higher or lower, based on the instructors’ evaluation as well as on the self-assessment and peer assessment carried out within the group.

Approval in the course unit also requires obtaining a minimum individual score of 40% in the exam.

Special assessment (TE, DA, ...)

The distributed assessment, carried out during the semester in which the course unit operates, is required for all students, regardless of the enrollment regime.

Student workers and their equivalents dismissed from classes must, at intervals to be agreed with the teachers, present the progress of their work, as well as present these, simultaneously with ordinary students, and carry out the theoretical tests for individual assessment provided for.

Classification improvement

Only the individual component (exam) can be improved.