1. Tutorial#3

2. Retrieval models Retrieval models match query with documents to:
separate documents into relevant and non-relevant class
rank the documents according to the relevance.
Boolean model
Vector space model (VSM)
Probabilistic models

3. Boolean model Boolean model is most common exact-match model
queries are logic expressions with document features as operands
In pure Boolean model, retrieved documents are not ranked.

4. Example D7 OR
D1,D2,D5
AND
D2,D4,D5,D6,D8 D7 OR
D2,D5

7. Vector space model (VSM)
Documents and queries are represented as vectors.
dj = (w1,j,w2,j,...,wt,j) q = (w1,q,w2,q,...,wt,q)
Each dimension corresponds to a separate term. If a term occurs in the document, its value in the vector is non-zero.

8. K5 K4 K3 K2 K1 K0 Q0 Q1 Q2 Q3 Q4 K5 K4 K3 K2 K1 K0 D0 D1 D2 D3 D4

9. Vector space model (VSM)
Several different ways of computing these values, also known as (term) weights, have been developed. One of the best known schemes is (tf-idf) weighting:

10. (tf-idf) weighting

11. Vector space model (VSM)

12. Example documents: D0:'How to Bake Bread Without Recipes',
D1:'The Classic Art of Viennese Pastry',
D2:'Numerical Recipes: The Art of Scientific Computing',
D3:'Breads, Pastries, Pies and Cakes : Quantity Baking Recipes',
D4:'Pastry: A Book of Best French Recipe‘
Keywords :
['bak','recipe','bread','cake','pastr','pie']

13. will generate a matrix 6 terms x 5 documents
'pie'
'pastr'
'cake'
'bread'
'recipe'
'bak' 1 D0 D1 D2 D3 D4

14. Query: "baking bread“ will generate a matrix 6 terms x 5 documents
'pie'
'pastr'
'cake'
'bread'
'recipe'
'bak' 1 D0 D1 D2 D3 D4

15. VSM Implementation VSMranker.java ranks documents for a query
Provides functions to develop different user interfaces
Stand alone usage needs document and query TDMs
java -cp ../java VSMranker cacm.tdm query.tdm 7
Retrieves top 7 documents for CACM queries

16. Ex#3 (solve in tutorial time)

17. References: