1. Information Retrieval
Lebanese University
Faculty of Economics and Business Administration – 1st Branch
Class: M1
Instructor: Dr. Lina A. Nimri

2. Course Text Book Modern Information Retrieval,
R. Baeza-yates and B. Ribeiro-Neto.,
Addison-Wesley and ACM Press, 1999,
ISBN: X

3. Introduction Modern Information Retrieval, Chapter 1
Ricardo Baeza-Yates, Berthier Ribeiro-Neto

4. Introduction (1) are maintained by a USA university and
Examples of information need in the context of the world wide web:
“Find all documents containing information on computer courses which:
(1) are offered by universities in South England, and
(2) are accredited by the BCS/IEE bodies,
To be relevant, the document must include information on admission requirements, and and phone number for contact purpose.”
“Find all docs containing information on college tennis teams which:
(1) are maintained by a USA university and
(2) participate in the NCAA tournament.
 Information Retrieval

5. Information Retrieval
Representation, storage, organisation, and access to information items
(Usually) keyword-based representation
User Information Need
Query
Documents
Useful or relevant information to the user
Set of retrieved documents
Search Engine
Retrieval System
Primary goal of an IR system
“Retrieve all the documents which are relevant to a user query, while retrieving as few non-relevant documents as possible.”

6. Data Retrieval
Determine which documents contain the keywords in the user query is not always enough to satisfy the user information need.
Data Retrieval retrieves objects which satisfy clearly defined conditions, such as regular expressions or relational algebra expressions.
Data Retrieval system deals with data with well-defined structure and semantics

7. Information Retrieval System
Retrieving information about a subject
Deals with natural language text which is not well structured and could be semantically ambiguous
It must interpret the contents of documents and rank them according to the degree of relevance to the user need.

8. Area of interest Digital Libraries Information experts
World Wide Web - Very difficult task
The hyperspace is vast
The absence of a well defined data model (format or representation form)

9. Effective retrieval
The effective retrieval of relevant information is directly affected by:
The user task
The logical view of the document (document’s representation) adopted by the retrieval system.

10. User tasks Pull technology Push technology
User requests information in an interactive manner
3 retrieval tasks
Browsing (hypertext)
Retrieval (classical IR systems)
Browsing and retrieval (modern digital libraries and web systems)
Push technology
automatic and permanent pushing of information to user
software agents
example: news service
filtering (retrieval task) relevant information for later inspection by user

11. Pulling
The user can browse the documents when his main objectives are not clear in the beginning and whose purpose might change during the interaction with the system.
Combination of retrieval and browsing is not yet a well established approach.
Retrieval
Browsing
Database

12. Documents Unit of retrieval A passage of free text
composed of text, strings of characters from an alphabet
composed of natural language
newspaper article, a journal paper, a dictionary definition, messages
size of documents
arbitrary
newspaper article vs. journal paper vs.

13. What is a document?

14. Representation of documents
Documents are represented thru a set of index terms or keywords or term descriptors
extracted directly form text
specified by human subjects (information science)  metadata
Most concise representation
Poor quality of retrieval
Full text representation
Most complete representation
High computational cost
Large collections
Reduce set of representative keywords
Elimination of stop words
Stemming
Identification of noun phrases
Further compression
Document term descriptors to access texts
Generation of descriptors for text
By hand
By analysing the text

15. Logical View of the documents
Accents
spacing
Noun
groups
Manual
indexing
Docs
stopwords
stemming
structure
structure
Full text
Index terms

16. The retrieval functions
Information need
Documents
Formulation
Indexing
Query
Document representation
Relevance feedback
Retrieval functions
Retrieved documents

17. Queries Information Need: Simple queries Boolean queries
composed of two or three, perhaps even dozens, of keywords
e.g., as in web retrieval
Boolean queries
“neural networks AND speech recognition”
Context Queries
Proximity search, phrase queries
User term descriptors characterising the user need

18. Best-Match retrieval Document term descriptors to access texts
Compare the terms in a document and query
Compute similarity between each document in the collection and the query based on the terms that they have in common
Sorting the documents in order of decreasing similarity with the query
The outputs are a ranked list and displayed to the user - the top ones are more relevant as judged by the system
User term descriptors characterising the user need

19. Conceptual view of text retrieval system
Queries
Similarity
Computation
Documents
Retrieved
Documents

20. Expanded view of text retrieval system
Queries
Indexed
Documents
Documents
Indexing
Similarity
Computation
Retrieved
Documents
Ranked
Documents

21. Process of retrieving info
User Interface
Text
User feedback
User need
Text
Text Operations
Logical view
Logical view
Document Repository
Manager
Query Operations
Indexing
Inverted file
Query
Indexing languages – for representing queries and representing documents
Similarity algorithms
Similarity Computation (Searching)
Index
Text repository
Retrieved docs
Ranked docs
Ranking

22. IR + Link Structure + Name Interpretation
Key Topics
Indexing text documents
Retrieving text documents
Evaluation
Query reformulations
Search Engines =
IR + Link Structure + Name Interpretation

23. Information Retrieval vs Information Extraction
Given a set of query terms and a set of document terms select only the most relevant documents [precision], and preferably all the relevant [recall].
Information Extraction
Extract from the text what the document means.
IR systems can FIND documents but need not “understand” them