Information Retrieval - Academic Year 2018/2019

General Information

Teacher : Paolo Ferragina
Course ID: 289AA
CFU: 6 (first semester)
Language: English
Question time: After lectures it will be announced via Twitter
Official Lecture's Log: Here it is the registro.
News about this course will be distributed via a Tweeter-channel

Goals

Study, design and analysis of IR systems which are efficient and effective to process, mine, search, cluster and classify documents, coming from textual as well as any unstructured domain. In the lectures, we will:

study and analyze the main components of a modern search engine: Crawler, Parser, Compressor, Indexer, Query resolver, Query and Document annotator, Results Ranker;

dig into some basic algorithmic techniques which are now ubiquitous in any IR application for data compression, indexing and sketching;

describe few other IR tools which are used either as a component of a search engine or as independent tools and build up the previous algorithmic techniques, such as: Classification, Clustering, Recommendation, Random Sampling, Locality Sensitive Hashing.

Week Schedule of the Lectures

Week Schedule
Day	Time Slot	Room
Monday	11:00 - 13:00	L
Tuesday	9:00 - 11:00	L1

Exam

The exam will consist of a written test plus an oral discussion on the exercises.

Date	Room	Text	Notes
29 oct 2018, 16:00-18:00	C1, L1	First Midterm exam: text, results, solution	Students that got a rank >= 16 can participate to the second midterm exam. Friday 9th November, hr 16:00, room L1, correction of the written exam.
19 dec 2018, 16:00-18:00	A1, L1	Second Midterm exam: text, results, solution
18 jan 2018, 09:00-11:00	A1	text, results, solution
15 feb 2018, 09:00-11:00	A1	text, results, solution

Books

[MRS] C.D. Manning, P. Raghavan, H. Schutze. Introduction to Information Retrieval. Cambridge University Press, 2008. [ link ]
Some copies of papers (linked below).
Some notes by Prof. Paolo Ferragina (linked below).

Content of the Lectures

Date	Argument	Refs
17.09.2018	Introduction to the course: modern IR, not just search engines! Boolean retrieval model. Matrix document-term. Inverted list: dictionary + postings. How to implement an AND, OR and NOT queries, and their time complexities.	Slides. Chapter 1 of [MRS]
18.09.2018	Web search engine: its structure, difficulties in their design and their epochs. The Web graph: some useful structural properties (such as Bow Tie). Crawling: problems and algorithmic structure. An example: Mercator.	Slides. Sections 19.1, 19.2, 19.4, 20.1, 20.2 of [MRS].
24.09.2018	Few useful algorithmic techniques for crawling the Web (and not only that!): Bloom Filter and Spectral Bloom Filter.	Slides. For doubts on Bloom Filter see paper.
25.09.2018	Other useful algorithmic techniques for crawling the Web (and not only that!): Consistent Hashing. Web graph properties and its compressed storage.	Slides of the previous lecture. Sect 19.1 and 19.2, 20.3 and 20.4 of [MRS].
01.10.2018	Locality-sensitive hashing: basics, hamming distance, proof of the probability bounds.	Slides
02.10.2018	Exact-duplicate documents: Karp-Rabin's rolling hash (with properties and error probability). Near-duplicate documents: Shingling, Jaccard similarity, min-hashing (with prob property), LSH on integer vectors, cosine-similarity among vectors of real-features.	slides. Sect 19.6 of [MRS]
08/10/2018	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.	slides. Chapter 4 of [MRS].
09/10/2018	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.	Slides. Reading a paper.
15/10/2018	Parsing: tokenization, normalization, lemmatization, stemming, thesauri. Statistical properties of texts: Zipf law: classical and generalized, Heaps law, Luhn's consideration.	Slides. Sect. 2.1, 2.2 and 5.1 of [MRS].
16/10/2018	Keyword extraction: statistical, chi-square test, Rake tool. Exact search: hashing. Prefix search: compacted trie, front coding, 2-level indexing.
22/10/2018	Exercises.
23/10/2018	Exercises.
	Midterm exam
05/11/2018	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.	Slides (new version). Chap 3 of [MRS].
06/11/2018	Query processing: soft-AND, skip pointers, caching, phrase queries. Tiered index. Posting list compression, codes: gamma, variable bytes (t-nibble), PForDelta and Elias-Fano.	Slide query, Slide integer compressors. Sect. 2.3 and 2.4 and 5.3 of [MRS] and Ferragina's notes (only the coders presented in class).
09/11/2018	Correction of the midterm exam
12/11/2018	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.	Slides.
13/11/2018	Succinct representation of binary trees and its navigational operations: heap like notation and LOUDS.	Slides.
19/11/2018	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.	Slides. Sect 6.2 and 6.3 and 7 from [MRS].
20/11/2018	Exact Top-K: WAND and blocked-WAND. Relevance feedback, Rocchio, pseudo-relevance feedback, query expansion. Performance measures: precision, recall, F1 and user happiness.	Chap 8 and 9 of [MRS]
26/11/2018	Random Walks. Link-based ranking: pagerank, topic-based pagerank, personalized pagerank, CoSim rank. HITS.	Slides. Chap 21 of [MRS]
27/11/2018	Exercises
3/12/2018	Projections to smaller spaces: Latent Semantic Indexing (LSI). Random Projections: Johnson-Linderstauss Lemma and its applications.	Slides. Chap 18 from [MRS].
4/12/2018	Semantic-annotation tools: basics, Wikipedia structure, TAGME and other annotators. Various approaches to text representation and their applications. Exercises	Slides
10/12/2018	A glimpse on Sport Analytics, and discussion of possible topics for a master thesis.	Slides and Thesis ideas.
11/12/2018	Exercises.

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