Organizing and Providing Access to Information
LIS 391D.2 (Unique # 42730)
Spring 1998
Friday 2-5 p.m.
SZB 556

(Doctoral Student Seminar)

Instructor: Ruth A. Palmquist
Office: SZB 562J
Office Hours: Fridays, before and after class and Wednesdays 1-3 p.m.,
and always by request at other times
Phone: Office 471-3839; Home 326-4016
Email: PALMQUIS@UTS.CC.UTEXAS.EDU

Graduate Teaching Assistant: Cecilia Salvatore
Phone: 471-2718
Email:cls@gslis.utexas.edu

COURSE DESCRIPTION

This seminar is designed to cover many of the basic current questions that arise while considering the research literature of organizing and providing access to information. Much of the emphasis will be on current digital techniques for providing for information storage and retrieval; however there is a conscious effort to provide a historical view of these current digital approaches. There are a confusing array of terms and concepts that exists around the issue of information retrieval. Within LIS, the primary focus has been bibliographic or document retrieval. With the current digitization of full text and graphics, there are competing issues which move the LIS community toward fact retrieval. We will endeavor to distinguish these approaches as we go. Remember, as we review the literature appropriate to the topic that you, as future researchers, will need to be sensitive to research questions, methodologies, and eventual conclusions and implications drawn from the literature we will cover.

Objectives for the seminar include:

  1. To develop an understanding of the record structures, text processing and theories necessary to the preparation of digital texts for organization and retrieval. (Some may also choose to venture into the realm of organizing and retrieving audio and visual information as well.
  2. To examine more recent approaches to the representation of information in a variety of formats for automated retrieval and storage.
  3. To examine several basic research methodologies frequently used to test the effectiveness/success of such automated systems of information storage and retrieval.
  4. To develop informed opinions on several issues concerning current state of the art information retrieval, e.g., metadata standards, evaluation issues, digital library developments.

COURSE REQUIREMENTS

You should expect a grade of B for acceptable doctoral level work; only an outstanding performance will be given a grade of A. For each graded effort above, there will be a more formal description of task assigned together with some indication of the criteria on which the effort will be graded. Returned work will be given + and - indicators. In calculating the final grade, these letter grades will be assigned a numeric index which will then be weighed according to the wei ght given the assignment. For those who receive a final course average falling between, say, an A- and a B+, the frequency and quality of class participation and interaction with the instructor will be used to determine whether the final average is pushe d to a higher or lower level.

Instructor reserves the right to decrease letter grades for each day an assignment is late, unless student has sought some prior understanding.

TEXT:

Sparck Jones, Karen and Peter Willett, eds. Readings in Information Retrieval. San Francisco, CA: Morgan Kaufmann Publishers, Inc., c1997. (Referred to below as S&W)

TENTATIVE CALENDAR:

Jan. 23

Read:

  1. Chap 1, 2, and 3 Intros in S & W
  2. Hutchins, pp. 93-97
  3. Foskett, pp. 111-134.

Jan. 30

Read:

  1. Tefko Saracivic's "Reflections on the past, future and limits of information science." SIGIR Forum (ACM) 31(2): 16-27. Fall, 1997.
  2. Fran Miksa's Library Cataloging and Bibliographic Control," Chaps. 1-3. Provided in class, together with some of his notes and illustrations.

Feb 6

Feb 13

Read:

  1. Luhn, in S&W, pp. 21-24 for simple statistical indexing
  2. Doyle, in S&W, pp. 25-28, for associative indexing

Feb 20

Read:

  1. Cleverdon, in S&W, pp. 47-59 on the Cranfield Tests
  2. Cleverdon & Mills, in S&W, pp. 98-110
  3. Salton & Lesk, in S&W, pp. 60-84

Feb. 27

Read:

  1. Saracevic, in S&W, pp. 143-166 on Relevance
  2. Schamber, Eisenberg, and Nilan (1990) provided
  3. Cooper, in S&W, pp. 191-204

Mar. 6

Read:

  1. Tague-Sutcliffe, in S&W, pp. 205-216,
  2. Lancaster, in S&W, pp. 223-246,
  3. Donna Harmon - TREC, in S&W, pp. 247-256,
  4. Callan, Croft, & Broglio, in S&W, pp. 436-439
  5. Salton & Buckley, in S&W, pp. 355-364, on Relevance Feedback,
  6. Tenopir & Cahn, in S&W, pp. 446-456, on Relevance Ranking

Mar. 13

Mar. 20

SPRING BREAK

Mar. 27

Read:

  1. W.S. Cooper, in S&W, pp. 265-267, on Boolean
  2. Vector Space Model, in S&W, pp. 273-280,
  3. Probabilistic, in S&W, pp. 273-280, 339-344,
  4. Daniels, et al, in S&W, pp. 135-142, and Belkin,
  5. Oddy, Brooks, in S&W, pp. 299-304 on ASK

Apr. 3

Read:

  1. Porter, in S&W, pp. 313-316, on Stemming,
  2. Text Processing, in S&W, pp. 317-322,
  3. Term Weighting, in S&W, pp. 323-328,
  4. Sparck Jones, in S&W, pp. 329-338, on Relevance Weighting

Apr. 10

Apr. 17

Apr. 24

May 1

          ORGANIZING AND PROVIDING ACCESS TO INFORMATION

Then						Now

Post Coordinate					Expert Systems in IR		     
     Early Manual Approaches		     		CODER (Fox)
	Optical co-incident				PLEXUS
	Edge-notched cards				     
     (reactions to from LIS side)		Recommender Systems	
							Firefly
Thesaurus Development				     
     Descriptors (String Indexing) 		Data Mining Approaches		    
     Syndetic Structures for				Neural Networks
							Genetic Algorithms
Classificatory Principles
     Eleanor Rosch
     Semantic Memory

Indexing Languages				Current State of Natural
     Cranfield Tests					Language Processing 
     Early NLP - Stemming				(NLP)
     		 Stopword Lists		      	     	SMART (Salton)
		 Weighting Schemes
		  Parsing			Web Engine Techniques
     Faceted Classification				Latent Semantic Indexing
     	UDC (Stephen Austin - PRECIS)		     	Hypertext - Classificatory
    	Bliss						     Efforts
	Colon Classification (CC)			Intelligent Search Agents
                            				Fuzzy Boolean
 Automatic Indexing (Luhn)				     
     Simple Statistical				METADATA
	vs.						GILS
     Word Association (Doyle)				Dublin Core
				     			Warwick
Relevance						Multimedia
     Precision						SGML/XML
     Recall						Other Standards?
     							     
Document Clustering				Graphical Mapping of
Automatic Retrieval Models				Retrieved Web Search
    
Vector-Based IR					Fact Retrieval Efforts
Probabilistic IR					
						Non-text Retrieval (Images)
Automatic Abstracting				     	Fingerprints
							Photographs
Fact Retrieval Efforts

CLASS LIST

Suellen Adams: suellenSrs@aol.com
Jon Aho: jonaho@gslis.utexas.edu
Mark Duffy: mjduffy@uts.cc.utexas.edu
Paula Geist: geist@gslis.utexas.edu
Stan Gunn: sgunn@gslis.utexas.edu
Wanda Jackson: wkj@mail.utexas.edu
Helene Jaillet: hfjaillet@mail.utexas.edu
Jenny Monesson: jennymo@earthlink.net
Susan Soy: ssoy@gslis.utexas.edu
Bob Strong: strong@gslis.utexas.edu
To all

Last updated on March 30, 1998

Comments to: cls@gslis.utexas.edu