1. CSE 635 Multimedia Information Retrieval
Chapter 1: Introduction to IR

2. Motivation
IR: representation, storage, organization of, and access to information items
Focus is on the user information need
User information need:
When did the Buffalo Bills last win the Super Bowl?
Find all docs containing information on college tennis teams which: (1) are maintained by a USA university and (2) participate in the NCAA tournament.
Emphasis is on the retrieval of information (not data)

3. Information retrieval
Motivation
Data retrieval
which docs contain a set of keywords?
Well defined semantics
a single erroneous object implies failure!
Information retrieval
information about a subject or topic
deals with unstructured text
semantics is frequently loose
small errors are tolerated
IR system:
interpret contents of information items
generate a ranking which reflects relevance
notion of relevance is most important

4. Comparison of different information systems
Evaluation: Precision versus Recall

5. IR at the center of the stage
Motivation
IR at the center of the stage
IR in the last 20 years:
classification and categorization
systems and languages
user interfaces and visualization
Still, area was seen as of narrow interest
Advent of the Web changed this perception once and for all
universal repository of knowledge
free (low cost) universal access
no central editorial board
many problems though: IR seen as key to finding the solutions!

6. The User Task Basic Concepts Retrieval information or data purposeful
needle in a haystack problem
Browsing
glancing around
Formula 1 racing; cars, Le Mans, France, tourism
Filtering (push rather than pull)
Retrieval
Browsing
Database

7. Logical view of the documents
Basic Concepts
Logical view of the documents
documents represented by a set of index terms or keywords
Document representation viewed as a continuum: logical view of docs might shift
Accents
spacing
Noun
groups
Automatic or
Manual
indexing
Docs
stopwords
stemming
Structure
recognition
structure
Full text
Index terms

8. The Retrieval Process Indexing Retrieval 4, 10 6, 7 5 8 8 2 Text User
Interface
4, 10
user need
Text
Text Operations
6, 7
logical view
logical view
Query
Operations
DB Manager
Module
Indexing
user feedback 5 8
inverted file
query
Searching
Index 8
retrieved docs
Text
Database
Ranking
ranked docs 2
Indexing
Retrieval

9. Applications of IR Specialized Domains Information filtering
biomedical, legal, patents, intelligence
Information filtering
Summarization
Cross-lingual Retrieval
Question-Answering Systems
Ask Jeeves
Web applications
shopbots
personal assistant agents
Text Mining
data mining on unstructured text
Multimedia IR
images, document images, speech, music

10. IR Techniques Machine learning
clustering, SVM, latent semantic indexing, etc.
improving relevance feedback, query processing etc.
Natural Language Processing, Computational Linguistics
better indexing, query processing
incorporating domain knowledge: e.g., synonym dictionaries
use of NLP in IR: benefits yet to be shown for large-scale IR
Information Extraction
Highly focused Natural language processing (NLP)
named entity tagging, relationship/event detection
Text indexing and compression
User interfaces and visualization AI
advanced QA systems, inference, etc.

11. Issues to be Addressed by IR
How to improve quality of retrieval
Faster indexes and smaller query response times
better understanding of user behaviour
interactive retrieval
visualization techniques