Programma non disponibile in italiano

INFORMATION RETRIEVAL

Code: 289AACredits: 6Semester: 1
Lecturers: Ferragina Paolo

Learning outcomes

Knowledge

The student who successfully completes the course will have the ability to design a simple search engine or one of the numerous text mining tools which are at the core of modern Web applications.

Assessment criteria of knowledge

The student will be assessed on his/her demonstrated ability to discuss the main course contents using the appropriate terminology.

Methods:

  • Final oral exam
  • Final written exam
  • Test use of the Lucene and/or Elastic Search software

Further information on the home page of this course.

Skills

Students will be able to evaluate a search engine and make design and SW choices related to IR applications

Assessment criteria of skills

Via written and oral exam

Behaviors

Students will be able to understand and evaluate pro/cons of IR tools and which algorithmic solutions are the best for their IR problems at hand.

Assessment criteria of behaviors

Via written and oral exams

Prerequisites

Basics of Algorithms, Maths and Programming

Teaching methods

Delivery: face to face

Learning activities:

  • attending lectures

Attendance: Advised

Teaching methods:

  • Lectures

 

Further details in the home page of the course.

Syllabus

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.

Bibliography

C.D. Manning, P. Raghavan, H. Schutze. Introduction to Information Retrieval. Cambridge University Press, 2008 Chapter 2 “Text compression” of Managing Gigabytes, I.H. Witten and A. Moffat and T.C. Bell, Morgan Kauffman, Second edition, 1999.

Course web page

http://didawiki.di.unipi.it/doku.php/magistraleinformatica/ir/ir16/start

Fonte: ESSETRE e Portale esami