Big Data Application & Analysis

Search Engine

Question:Consider the table of term frequencies for 3 documents denoted Doc1, Doc2,

Doc3 in Table 1.0.

For each document, compute the tf-idf weights for the following terms using the

idf values from Table 1.1.

cat

animal

iguana

bee

Table 1.0: Table of tf values

	Doc1	Doc2	Doc3
cat	27	4	24
animal	3	33	0
iguana	0	33	29
bee	14	0	17

Table 1.1: Table of idf values

term	dft	idft
cat	18,165	1.65
animal	6,723	2.08
iguana	19,241	1.62
bee	25,235	1.5

2. Recall the tf-idf weights computed previously. Compute the Euclidean normalized

document vectors for each of the documents, where each vector has four

components, one for each of the four terms.

3. With term weights computed previously, rank the three documents by computing

the score for the query cat iguana, based on each of the following cases of term

weighting in the query:

a. The weight of a term is 1 if present in the query, 0 otherwise.

b. Euclidean normalized idf

My own answer is the following, but can someone also write the detailed process for solving the problem! Thank you

car 44.55 6.6 39.6

Animal 6.24 68.64 0

iguana 0 53.46 46.98

Bee 21 0 25.5

2)

doc1 = [0.8974, 0.1257, 0, 0.4230]

doc2 = [0.0756, 0.7867, 0.6127, 0]

doc3 = [0.5953, 0, 0.7062, 0.3833]

3)

a) q = [1, 0, 1, 0]

score(q, doc1)= 0.8974,

score(q, doc2) = 0.6883,

score(q, doc3) = 1.3015

Ranking: doc3, doc1, doc2

b) q = [0.4778, 0.6024, 0.4692, 0.4344]

score(q, doc1) = 0.6883,

score(q, doc2) = 0.7975,

score(q, doc3) = 0.7823

Ranking: doc2, doc3, doc1

Can someone also write down the detailed process of solving this answer! Thank you, I don't want a straight answer! I want to solve the process of this problem!! Thank you very much!!