Exercises of Information Retrieval

This repository contains the exercises (and some of their solutions) of various test exams of the Information Retrieval (IR) course, taught by Prof. Paolo Ferragina.

Subjects of the course

Like the course, the various solutions will be divided into the following topics:

1. Introduction: Boolean retrieval model. Matrix document-term. Inverted list: dictionary + postings. How to implement an AND, OR and NOT queries, and their time complexities.
2. Crawling: Mercator, Bloom Filters, Consistent Hashing, Web graph.
3. Locality-Sensitive Hashing: K-means, Hamming distance, Locality Sensitive Hashing (LSH).
4. Index Construction: The issue of hierarchical memories: I/O-model. Index construction: multi-way mergesort, BSBI and SPIMI. Sketch on MapReduce. Distributed indexing: Term-based vs Doc-based partitioning. Dynamic indexing: two indexes, a cascade of indexes.
5. Documents Compression: Compressed storage of documents: LZ-based compression. Storage and Transmission of single/group of file(s): Delta compression (Zdelta), File Synchronization (rsync, zsync), and Set Reconciliation.
6. Parsing and Text Laws: Parsing: tokenization, normalization, lemmatization, stemming, thesauri. Statistical properties of texts: Zipf law: classical and generalized, Heaps law, Luhn's consideration.
7. Dictionary Search: Exact search: hashing with chaining, univeral hashing, cuckoo hashing. Prefix search: compacted trie, front coding, 2-level indexing. Edit distance via brute-force approach, or Dynamic Programming (possibly weighted). Overlap measure with k-gram index. Edit distance with k-gram index. One-error match. Wild-card queries (permuterm, k-gram). Phonetic match. Context-sensitive match.
8. Query Resolver: Query processing: skip pointers (with solution based on dynamic programming), caching, phrase queries. Zone index and tiered index. The auto-complete problem and its solutions for the top-1, top-2, ..., top-k strings.
9. Postings Compression: Posting list compression, codes: gamma, delta, variable bytes, PForDelta and Elias-Fano. Rank and Select data structures, two approaches: the case of B untouched and extra o(B) bits, and the case of Elias-Fano's approach with B compressed. Succinct representation of binary trees and its navigational operations (heap like notation), LOUDS.
10. Text Ranking: Text-based ranking: dice, jaccard, tf-idf. Vector space model. Storage of tf-idf and use for computing document-query similarity. Fast top-k retrieval: high idf, champion lists, many query-terms, fancy hits, clustering. Exact Top-K: WAND and blocked-WAND. Relevance feedback, Rocchio, pseudo-relevance feedback, query expansion. Performance measures: precision, recall, F1 and user happiness.
11. Web Ranking: Random Walks. Link-based ranking: pagerank, topic-based pagerank, personalized pagerank, CoSim rank, HITS.
12. Projections: Projections to smaller spaces: Latent Semantic Indexing (LSI). Random Projections: Johnson-Linderstauss Lemma and its applications.
13. Topic Annotator: Semantic-annotation tools: basics, Wikipedia structure, TAGME and other annotators. How to evaluate those systems.
14. Lucene: Introduction to Lucene.

Topics covered by the exams

In this table is shown which kind of exercises you may find in a specific test exam (denoted by the date in which it was taken). The numbers describe the topics as in the previous section.

WARNING: The following table is just a stub. Many exercises may be misclassified.
WARNING: Every exam taken before 2016 may contain exercises form a previous programme.

Test Date	1	2	3	4	5	6	7	8	9	10	11	12	13	14	Status
29/10/18		●	●	●	●		●
30/10/17		●	●		●		●				●
05/09/17				●	●		●				●
27/07/17							●		●	●	●			●
29/06/17							●		●	●	●			●
12/06/17				●					●		●		●	●
01/02/17														●
12/01/17		●	●		●					●	●
10/01/17														●
02/09/16		●	●		●		●
27/06/16		●					●		●		●
01/02/16			●		●		●						●
11/01/16			●		●		●		●		●		●
10/09/15		●			●		●			●
20/07/15										●
29/06/15		●											●
05/06/15									●	●	●	●
09/02/15							●			●
16/01/15	●						●
29/07/14				●
30/06/14									●
09/06/14							●	●			●
29/01/14									●			●
08/01/14		●					●
16/07/13							●
25/06/13										●	●
12/02/13										●
10/01/13		●										●
03/09/12		●								●
23/07/12		●									●
08/06/12						●				●
01/02/12							●
11/01/12		●								●
07/12/11	●
01/09/11	●	●								●
20/07/11
24/06/11							●		●
21/02/11		●
01/02/11		●

Solutions file

For the latest PDF file, see the releases page.

You can otherwise build it yourself, using the following commands:

git clone https://github.com/flandolfi/IR-exercises.git
cd IR-exercises/
make

How to contribute

Any kind of contribution is welcome! If you wish to add a missing solution, follow these instructions:

1. Fork this repository;
2. Create a .tex file containing:
   • The text of the problem, preceded by the LaTeX macro \exercise;
   • The solution of the problem, preceded by the LaTeX macro \solution;
3. If you need a package, add it in the IR-exercise.tex file, using \usepackage{<package>};
4. Place the file in the specific folder for the subject of the exercise you have solved;
5. Append your name in the \author{<name>} field in the IR-exercise.tex file, preceded by \and;
6. Submit a pull request!

Thank you for your contribution! 😊

Links

• "Introduction to Information Retrieval", by C. D. Manning, P. Raghavan and H. Schütze (the main reference of the course, freely available online).
• "Mining of Massive Datasets", by J. Leskovec, A. Rajaraman and J. D. Ullman. This book contains most of the topics of the course which are not covered by the other book (freely available online).
• If you are following also the Algorithm Engineering course at University of Pisa, check out this other exercises repository!