1. Suppose that there is a standard IR evaluation data set containing 1000 documents. Assume that a particular query, Q, in this data set is deemed to be relevant to the following 20 documents in the collection: REL={d1,d5,d10,d88,d150,d200,d210,d250,d300,d400,d405,d472,d500,d501,d545,d600,d635,d700,d720,d800} Two different retrieval systems S1 and S2 are used to retrieve ranked lists of documents from this collection using the query. The top 10 retrieved documents for these two systems are given below (each list is in decreasing order of relevance). RET(S1)=d2,d5,d150,d250,d11,d33,d50,d600,d500,d520 RET(S2)=d250,d400,d150,d210,d999,d3,d501,d800,d205,d300 (a) Compute the Precision and the Recall for each system as a function of the number of documents returned (for 1 document returned, 2 documents returned, etc.) (b) Compute the F-score of the two systems, F-score is a single metric combines precision and recall (check the extra slides on the definition of precision, recall, and F-score, by default F-score is F1, which means with =1 )