2. Techniques of Information Retrieval
Tariq Hassan & Sabahat

3. Road Map :
• What is IR ?
• Why & How it works?
• Evaluation Techniques
• Global & Local Methods
1. Relevance Feedback
2. Probabilistic Relevance Feedback
3. Indirect Relevance Feedback
4. Rocchio Algorithm
5. Linear Classifiers
6. Naïve Bayes Text Classification
Question & Discussion

4. What is IR? Why & How?
• Information needed to satisfy user.
• Why?
Due to different formats of Data.
• How?
StopList
Stemming
Inverse Document Frequency
Word Counts

5. What is IR? Why & How?
Generally IR used in 3 scenarios
1. Web search
2. Personal IR ( Text Classification )
3. Enterprise Level

6. Evaluation Techniques
• Why?
• How?
Relevant & Non Relevant Documents
Precision And Recall Methods
P = # (relevant Items Retrieved)
#(retrieved Items)
R = #(relevant Items Retrieved)
#(relevant Items)

7. Methods:
1. Global Methods
Reformulation Queries
2. Local Methods
Relative to the initial results against any query

8. Local Methods
1. Relevance Feedback
2. Probabilistic Relevance Feedback
3. Indirect Feedback
1. Relevance Feedback
Feedback given by the user about the relevance of the
documents in the initial set of results.
1. Relevance Feedback
2. Probabilistic Relevance Feedback
PRF is implementing by building a classifiers.
1. Relevance Feedback
2. Probabilistic Relevance Feedback
3. Indirect Relevance Feedback
Without user interventions.
1. By using user actions.
2. By using user Histories or Logs

9. Conclusion :
Relevance Feedback
Assumption:
User have initial knowledge
Issues :
Misspelling
Cross Languages
Mismatch Vocabulary

10. Rocchio Algorithm
Incorporates the relevance feedback
mechanism in vector space model.
Also uses the
Cosine Similarity Function
Euclidean Mechanism

11. Example

12. Outcome
• Relevance Feedback plays an important
role to understand the user requirements.
• Rocchio Algorithm is not the best but the
optimized and better option due to its
simplicity and good results.
• Have a significant importance with respect
to content based systems.

13. Classification Problems
• Given:
– A document d
– A fixed set of categories:
Sports, Informatics, literature, medical, entertainment
– A training set of documents each labeled
with its class
• Determine:
– A learning method or algorithm which will
enable us to learn a classifier
– For a test document dT we have to determine
its category

14. Classification Techniques
• Manual (a.k.a. Knowledge Engineering)
–typically, rule-based expert systems
• Machine Learning
–Naïve Bayesian (Probabilistic)
– Decision Trees (Decision Structures)
– Support Vector Machines (Linear Classification)

15. Document Representation
• Binary Representation
• Frequency Representation
• TF*IDF Representation

16. Naïve Bayes document
classification example
• Probabilistic
– Prior vs Posterior
• Bernoulli Model
– Feature vector with binary elements
• Multinomial Model
– Integers representing frequency of
words

18. Classify the document

19. Naïve Bayes classfication
• Very fast learning and testing
– Why?
• Low storage requirements
• Very good in domains with many
equally important features
• More robust to irrelevant features
than many learning methods

20. Linear Classification
• Documents as labeled vectors
• Documents in the same class form a
contiguous region of space
• Documents from different classes don’t
overlap (much)
• Learning a classifier: build surfaces to
delineate classes in the space

21. Support Vector Machines
• Find a linear hyperplane (decision boundary) that
will separate the data

22. Support Vector Machines
• OnePossibleSolution
B1

23. Support Vector Machines
• Anotherpossiblesolution
B2

24. Support Vector Machines
• Otherpossiblesolutions
B2

25. Support Vector Machines
• Which one is better? B1 or B2?
• How do you define better?
B1
B2

26. Support Vector Machines
• Find hyperplane maximizes the margin
B1
B2
b11
b12
b21
b22
margin

27. Support Vector Machines
B1
B2
b11
b12
b21
b22
margin
Support
Vectors

28. Support Vector Machines
B1
b11
b12
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

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
1bxwif1
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)( 
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xf 2
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2
Margin
w


29. Support Vector Machines
B1
b11
b12
0 bxw

1 bxw
 1 bxw
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

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xf 2
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2
Margin
w


31. Bottom Line
• Which classifier do I use for a given document
classification problem?
 Answer : Depends
 How much training data is available?
 How simple/complex is the problem?
 How noisy is the data?
 How stable is the problem over time?
 For an unstable problem, its better to use a
simple and robust classifier.