Information Retrieval
Objectives
Knowledge
- Learn the concept of information relevance.
- Analyze text data.
- Learn how to rank information by relevance.
- Understand evaluation protocols.
Know-how
- Implement information retrieval models.
- Ability to adapt and improve components of a search engine.
- Deploy search engines with large-scale datasets.
- Design evaluation protocols and evaluate search engines.
Soft-Skills
- Select the right IR techniques for particular problems.
- Design information retrieval systems.
- Ability to do critical thinking about retrieval results.
General characterization
Code
12077
Credits
6.0
Responsible teacher
João Miguel da Costa Magalhães
Hours
Weekly - 4
Total - 2
Teaching language
Português
Prerequisites
Programming skills. Python preferably.
Linear algebra and probability courses.
Bibliography
Main reference: Dan Jurafsky and James H. Martin, Speech and Language Processing (3rd ed. draft) https://web.stanford.edu/~jurafsky/slp3/
Complementary reference: C. D. Manning, P. Raghavan and H. Schütze, “Introduction to Information Retrieval”, Cambridge University Press, 2008. https://nlp.stanford.edu/IR-book/information-retrieval-book.html
Teaching method
Available soon
Evaluation method
Available soon
Subject matter
1. Introduction
2. Text processing, NGRAMS, cosine distance
3. Language models
4. Evaluation
5. Pseudo relevance models
6. Classification tasks: sentiment, category, spam
7. Learning to rank
8. Word embeddings
9. Contextual embeddings
10. Information extraction
11. Question answering
12. Ethics in Computational NLP