DATA AND KNOWLEDGE

ENGINEERING GROUP

General Information

This web page gives information on the lecture 'Information Retrieval' which is held during the winter term 2021/2022 by Andreas Nürnberger. A moodle link will be provided, which will contain all the necessary information and content of the course and will be updated regularly.

Please note, you need to mandatorily register via LSF at https://lsf.ovgu.de (you need an OVGU aacount) for the exercises group choice.

The course content is available via Moodle (you need an OVGU account):

https://elearning.ovgu.de/course/view.php?id=11410

Enroll with the key: searchMe2122

Read the FAQs, this will hopefully address many questions that you might have, else email bhavana.malla@st.ovgu.de for any organizational questions.

Note that the lectures start on 14th Oct '21 (H6-G44) and deadline for first sheet is 15th Oct '21 11 AM CET via Moodle.

The questions are easy enough to be handled without having heard the lecture.

Exercise classes start from the week, 18th Oct '21 onward.

Important: Currently, the term is planned as an offline term. Therefore there is at the moment no plan to offer exclusive online exercises. 

(Last Updated on 11th Oct, 16:30 CET) 

Information retrieval focuses on obtaining, extracting or mining information from a large collection of unstructured data, e.g. in form of text documents, images or videos. Information retrieval concepts are applied in web search engines, digital libraries and multimedia archives such as image and video databases. In this course the foundations of information retrieval will be introduced and illustrated on some specific application areas.

Master students, please note that this course is 5CP only!

Requirements for Participation in the Final Exam

All students are required to participate in the exercise classes. Every week, there will be an assignment sheet that will be handed out one week in advance. This sheet has to be prepared by every student and will be discussed in class. There are two different types of exercise tasks: theoretical tasks and a programming task. The programming assignments can be solved in small groups of up to three students and must be sent in before the respective deadline. Prerequisites for a written exam and a 'Schein' is fulfillment of the following criteria:

• at least 66% of individual votes for all theoretical tasks,
• at least  66% of the group programming task (can be done in groups- 4-5 students per group)
• at least two presentations of a solution in front of the class.

For acquiring the "Schein" you have to write and pass the exam.

A general reminder: In accordance with the examination rules, we offer each student exactly one examination date (oral or written) each term. The registration for a follow-up examination is only possible in the next term (i.e. after 6 months). As soon as a student has registered for an exam, either by using the LSF for written exams or by filling in the information on an examination list for oral exams (or filling out a registration form), this is counted as the agreed examination date. If it is cancelled, the rule above applies.

Dates and Rooms

 Please register for the exercises via LSF! (Registrations are open!)

 Please also check LSF for more updated information on the times and rooms.

Teaching Staff

If you have any questions about the lecture or the exercises, please contact us via e-mail:

• Andreas Nürnberger
  E-Mail: andreas.nuernberger@ovgu.de
• Sayantan Polley
  E-Mail: sayantan.polley@ovgu.de
• Afra'a Ahmad Alyosef
  E-Mail: afraa.ahmad-alyosef@ovgu.de
• Tutors
• tba

Materials

Will be provided in Moodle (link to be given).

Additional Materials

• Additional Slides on Document Pre-precessing (Courtesy: Johannes Schwerdt)
• Porter Stemmer
• Porter Paper
• LSI Tutorial

Literature

• Introduction to Information Retrieval, C.D. Manning, P. Raghavan, H. Schütze, Cambridge University Press, 2008. (Online-Version)
• Search User Interfaces, Marti Hearst, Cambridge University Press, 2009. (Online-Version)
• Soft Computing in Information Retrieval, Fabio Crestani and Gabriella Pasi, Physica Verlag, 2000.
• Modern Information Retrieval, Ricardo Baeza-Yates and Berthier Ribiero-Neto, Addison Wesley, 1999.
• Foundations of Statistical Natural Language Processing, Chris Manning and Hinrich Schütze, MIT Press, Cambridge, MA, 1999.
• Information Retrieval: Data Structures and Algorithms, William B. Frakes and Ricardo Baeza-Yates, Prentice-Hall, 1992.