IR-part2

84 evaluang ranked results evaluaon of a result set

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Unformatted text preview: nce Query: best car insurance Term Query tf- tf-wt raw df idf Document wt n’liz tf-raw e tf-wt Prod wt n’liz e auto 0 0 5000 2.3 0 0 1 1 1 0.52 0 best 1 1 50000 1.3 1.3 0.34 0 0 0 0 0 car 1 1 10000 2.0 2.0 0.52 1 1 1 0.52 0.27 insurance 1 1 3.0 3.0 0.78 2 1.3 1.3 0.68 0.53 1000 Exercise: what is N, the number of docs? Doc length = 12 + 0 2 + 12 + 1.32 ≈ 1.92 Score = 0+0+0.27+0.53 = 0.8 Introduc)on to Informa)on Retrieval Compu*ng cosine scores Sec. 6.3 Introduc)on to Informa)on Retrieval Summary – vector space ranking §༊  Represent the query as a weighted k- idf vector §༊  Represent each document as a weighted k- idf vector §༊  Compute the cosine similarity score for the query vector and each document vector §༊  Rank documents with respect to the query by score §༊  Return the top K (e.g., K = 10) to the user Introduc)on to Informa)on Retrieval...
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