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Information Retrieval
Instructor: Muh-Chyun Tang
Dept. of Library and Information Science,
Nationa Taiwan University

Course description
The course is designed to provide an introduction to the use, design and evaluation of information (IR) systems. It covers major components in the IR process such as search strategies, indexing, IR models and IR evaluation. Students will also acquire hand-on experiences with IR evaluation and designing a digital library system. Special attention will be given to the comparision of different indexing methods and IR models and how they might be complement each other.

Course objectives
After this class, you are able to
1. Have better search skills (for both professional databases and web search engines)
    Query construction and use of search strategies
2. Acquire skills in IR evaluation
3. Basic understand of how search engine works
    Automatic indexing
    Ranking algorithms
4. Design and implement an online digital collection using content management system

Schedule

Week	Topic	Note
W1	Introduction to syllabus History of IR; data vs. information retrieval	Information access: browsing, seaching, and recommendation
W2	Advanced search with PubMed; introduction to search features with PubMed/Ovid/Ebsco/EMBASE Discussion of your search demo project	O'Connor, p.61-65 ; Bell, p. 9-18; browse PubMed help and PubMed tutorial (see references)
W3	Search strategies tactics ; PICO; Camtasia demo (laptop) Discussion of your search demo project	(Hersh, 2003).p.191-194 (subheading and explode);
W4	Indexing exhaustivitiy vs. specificity Automatic index basic (text analysis, term weighting)	Lancaster 2003 p. 252-258: Natural Language vs. controlled vocabulary; Salton & McGill, p.59-63; Soergel, 1985,P.328-338.
W5	Search feature/command demo due Lab or laptop TF*IDF tool	Presentation of your seach demo video
W6	IR evaluatoin Discussion of your second (Simulated search evaluation)	Hersh, pp.95-113, relevance based evaluation
W7	IR models I: Boolean; term weighting and vector space model; similarity measures; Discussion of your IR evaluation project Homework/TF*IDF automatic indexing due	Hersh p. 270-272; appendix I-III (Vector space); Wickens; p.12-13 (basic vector operation) PubMed demo presentation due
W8	Simulated search evaluation presentation	Hersh, pp. 184-190 (relevant feedback); Relevance feedback in class exercise
W9	Facet analysis and information architecture Wordpress demo at computer lab Discussion of your DL project	林雯瑶, 2006 Facet structure
W10	Relevance feedback and query expansion; IR model II: Probability model Discussion of your DL project	Manning et.al. (2008). 201-211
W11	IR model: probablitic and language models Discussion of your DL project	Query likelihood in class exercise
W12	Lab session with your DL project
W13	DL assignment presentation	DL assignment presentation due
W14	Web search and link structure	(Easley, Kleinberg, 2010) Link Analysis and Web search (351-365)
W15	Final review
W16	Final Exam

Assignments and Grading
Attendance to all class sessions is mandatory. Your grade will be judged based on your participation, homeworks, and in-class assignments. For your group projects will be judged by both the instructor

A. Homework and participation (10%)
There will be one homework on the practice of text tokenization and TFxIDF calculation.

B. Group projects: Students will form into groups of 3 to 5 to conduct 3 group projects. For each project, besides the group reports,
*** each group member should prepare an one to two paragraphs personal report explaining your contributions and what you have learned from the assignments.

1. Search feature/command demo (accounts for 10% of your final grade)
create and present a video demo that explains a search tactics or function available with Ovid/medline, Ebsco/Medline, Embase, or Scopus.
See example: PubMed clinical queries

2. Simulated literature search evaluation (20%)
a. To obtain the search topics, interview two users (preferably graduate students or faculty members in sciences), each on one research topic they are interested in.
Collect from each user: a search statement and associated query terms that you both agree best represent her information need. Also try to characterize her information need using attributes such as "topic familiarity" and "uncertainty".
b. For each search topic, submit the queries on the user’s behalf to Google Scholar , Microsoft academic search, Semantic scholar or other major citation databases (e.g. Scopus, WOS). Collect the first 30 links from each of the two returned sets.
c. Find out the degree of overlap among the two returned sets.
d. Mix the non-duplicative (30X2, maximum) links together and strip the graphic cues.
This is done so that the user will not be able to tell which search engine each link is from.
e. For each link, marks its original and rank position.
f.  Present the URLs in Microsoft Word files that allow the users to examine the actual webpage by clicking
on its hyperlink. Ask them to judge the relevance (topical as well as situational) of the pages based on a 0-4 scale (0 stands for not relevant at all; 4, very relevant).
g. Create an EXCEL or SPSS data file to input the relevance scores.
h. Compare the performance of the search engines based on 1) Mean Average Precision,
2) CG and DCG
I. Next submit the same query to Scopus and Web of Science and conduct a domain analysis, in which you will identify the publication trends, major authors, institues, journals, countries, and disciplines that have published in this area.

3. Digital library construction (30%)
Each group will build a functional online digital library collaboratively using WordPress, Joomla , or Greenstone digital library (GSDL) open source content management system.
DL_project_exampl1 DL_project_example2   DL_project_example3
The project consists of three components: the implantation of a digital collection on the topic of your own choosing, a written report (5-6 pages) and an oral presentation of the project.

The digital collection should include:
a. A minimum of 70 documents representative of different document formats such as pdf, word, and html.
b. An index structure that enables browsing of the collection
c. The provision of faceted and fielded search
The written report (4-6 pages) should:
d. Explain the aim, purpose, sources, intended users and their information needs of the collection.
It is better that you come up with an institutional context (real or imaginary) for the use of the collection.
e. Define your selection and indexing policies (human and machine indexing components; metadata structure) based on the aim and purpose stated above.
f.  Include a graphic presentation of the browsable index structure and the rationales behind your design
(i.e. explain why you choose certain browsable facets and searchable fields to represent your collection)

C. Final exam (30%)
The exam is based on the lecture notes and readings, a review will be given before the exam to help you prepare for it.

References
    Bell, S. S.(2006). Librarian's guide to online searching.
    Bhavani, S. K. K. Drabenstott, D. Radev (2000). Towards a unified framework of IR tasks and strategies.
    Manning, Raghavan, Schutze (2008). Introduction to Informaiton Retrieval. Cambridge.
Chowdhury, G.G. (2004), Introduction to modern information retrieval. London: Facet publishing.
    William, H. R. (1996). Information retrieval : a health and biomedical perspective. New York: Springer-Verlag New York, Inc.
    Salton & McGill (1983). Introduction to modern information retrieval. McGraw-Hill..
Growssman, and Frieder (2004). Information retrieval: algorithms and Heuristics
    Belew, Richard K. (2000). Finding out about: a cognitive perspective on search engine technology and the WWW. Cambridge: Cambridge University Press.
    O'Connor, B. (1996). Explorations in indexing and abstracting.
Lancster, 2003. Indexing and abstracting in theory and practice.
    Evaluation of Web-Based Search Engines Using User-Effort Measures. Availableonline: http://libres.curtin.edu.au/libres13n2/tang.htm
    Ian H. Witten, David Bainbridge (2003). How to Build a Digital Library, Amsterdam: Morgan Kaufmann Publishers.
Janach, D., M. Zanker, A. Felfernig, G. Friedrich (2011). Recommender systems: an introduction. Cambridge.
Soergel (1985). Organizing information: principles of data base and retrieval systems Academic Press Professional, Inc. San Diego, CA.
Camtasia
Download ; Video tutorial
PubMed
PubMed tutorials, available http://www.nlm.nih.gov/bsd/disted/pubmed.html
OVID SP tutorial from Yale University Library
SCOPUS tutorial
PubMed help Available at Online_help
http://www.ncbi.nlm.nih.gov/books/bookres.fcgi/helppubmed/pubmedhelp.pdf
Greenstone: The software can be downloaded at
http://www.greenstone.org/cgi-bin/library?e=p-en-home-utfZz-8&a=p&p=download
Manuals (the “User’s guide” is most relevant to our purpose) http://greenstone.sourceforge.net/wiki/index.php/M anual