Faculty of Mathematics and Physics

*"Information retrieval is the task of searching a body of information for objects that
statisfied an information need."*

This course is offered at the Faculty of Mathematics and Physics to graduate students interested in the area of information retrieval, web search, document classification, and other related areas. it is based on the book by Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze Introduction to Information Retrieval. The course covers both the foundations of information retrieval and some more advanced topics.

SIS code: NPFL103

Semester: winter

E-credits: 6

Examination: 2/2 C+Ex

Lecturer: Pavel Pecina, pecina@ufal.mff.cuni.cz

Language: The course is taught in **English**. All the materials are in English, the homework assignments and exam can be completed in English or Czech.

- Mondays, 15:40-17:10 (18:50) S11, Malostranské nám. 25
- Consultations upon request by email.

Oct 15 The lecture room has changed to S11.

Oct 08 The course will start on Oct 8 at 15:40 in S1.

1. Introduction, Boolean retrieval, Inverted index, Text preprocessing Slides

2. Dictionaries, Tolerant retrieval, Spelling correction Slides

3. Index construction and index compression Slides

4. Ranked retrieval, Term weighting, Vector space model Slides

5. Ranking, Complete search system, Evaluation, Benchmarks Slides 1. Vector space models

6. Result summaries, Relevance Feedback, Query Expansion Slides

7. Probabilistic information retrieval Slides

8. Language models, Text classification Slides

9. Vector space classification Slides

10. Document clustering Slides 2. Retrieval frameworks

11. Latent Semantic Indexing Slides

12. Web search, Crawling, Duplicate detection, Spam detection Slides

No formal prerequisities are required. Students should have a substantial programming experience and be familar with basic algorithms, data structures, and statistical/probabilistic concepts.

To pass the course, students need to complete two homework assignments and a written test. See grading for more details.

**Note:** The slides available on this page might get updated during the semestr. For each lecture, any updates will be published before the lecture starts.

Oct 08 Slides

- definition of information retrieval
- boolean model, boolean queries, boolean search
- term incidence, inverted index, dictionary, postings, index construction, postings list intersection
- text processing, tokenization, term normalization, diacritics, case folding, stop words, lemmatization, stemming, Porter algorithm
- phrase queries, biword index, positional index, proximity search

Oct 15 Slides

- dictionary data structures, hashes, trees
- wildcard queries, permuterm index, k-gram index
- spelling correction, correcting documents, correcting queries, spellchecking dictionaries
- edit distance, Levenshtein distance, weighted edit distance, query spelling correction
- Soundex

Oct 22 Slides

- RCV1 collection
- sort-based index construction, Blocked sort-based indexing, Single-pass in-memory indexing
- distributed indexing, Map Reduce, dynamic indexing
- index compression, Heap's law, Zipf's law, dictionary compression, postings compression

Nov 29 Slides

- ranked retrieval vs. boolen retrieval, query-document scoring, Jaccard coefficient
- Bag-of-words model, term-frequency weighing, document-frequency weighting, tf-idf, collection frequency vs. document frequency
- vector space model, measuring similarity, distance vs. angle, cosine similarity
- length normalization, cosine normalization, pivot normalization

Nov 5 Slides 1. Vector space models

- ranking, motivation and implementation, document-at-a-time vs. term-at-a-time processing
- tiered indexes
- query processing, query parser
- evaluation, precision, recall, F-measure, confusion matrix, precision-recall trade-off, precision-recall curves, average precision, mean everage precision, averaged precision-recall graph, A/B testing
- existing benchmarks

Nov 12 Slides

- static summaries, dynamic summaries
- relevance feedback, Rocchio algorithm, pseudo-relevance feedback
- query expansion, thesaurus construction

Nov 19 Slides

- probabilistic approaches to IR
- Probabilistic Ranking Principle
- Binary Independence Models
- Okapi BM25

Nov 10 Slides

- language models for IR
- smoothing (Jelinek-Mercer, Dirichlet, Add-one)
- text classification
- Naive Bayes classifier
- evaluation of text classification, micro averaging, macro averaging,

Dec 17 Slides

- vector space classification
- k Nearest Neighbors
- linear classifiers
- Support Vector Machines

Dec 17 Slides 2. Retrieval frameworks

- document clustering in IR
- vector space clustering
- K-means clustering
- setting number of clusters
- evaluation of clustering
- hierarchical clustering, dendrogram, cluster similarity measures

Jan 7 Slides

- Singular Value Decomposition
- dimensionality reduction
- LSI in information retrieval
- LSI as soft clustering

**Note:** Detailed specification of the assignments will be distributed via email. The deadlines are hard, no extensions are allowed.

Deadline: Dec 10 23:59 100 points

Design, develop and evaluate your own retrieval system based on vector space models.

Deadline: Jan 6 23:39 100 points

Design, develop and evaluate a state-of-the-art retrieval system using an off-the-shelf retrieval framework.

- There are two homework assignments during the semester.
- The assignments are to be worked on independently and require a substantial amount of programming, experimentation, and reporting to complete.
- The students will present their solutions during the practicals in 10 minute presentations.
- The assignments are graded by 0-100 points each.

- The exam has a form of a written test, scheduled at the end of semester.
- The test includes approximately 20 short-answer questions covered by the topics discussed during the lectures.
- The maximum duration of the test is 90 minutes.
- The test is graded by 0-100 points.

- Completion of both the homework assignments and exam is required to pass the course.
- The students need to earn at least 50 points for each assignment and at least 50 points for the test.
- The final grade will be based on the average results of the exam and the two homework assignments, all three weighted equally:
- ≥ 90%: grade 1 (excellent)
- ≥ 70%: grade 2 (very good)
- ≥ 50%: grade 3 (good)
- < 50%: grade 4 (fail)

- No plagiarism will be tolerated.
- All cases of plagiarism will be reported to the Student Office.

Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze
*Cambridge University Press*, 2008, ISBN: 978-0521865715.

Available online.

David A. Grossman and Ophir Frieder,
*Springer*, 2004, ISBN 978-1402030048.

Ricardo Baeza-Yates and Berthier Ribeiro-Neto,
*Addison Wesley*, 1999, ISBN: 978-0201398298.