4. Probabilistic Retrieval Model We are interested in using the following document-term matrix and the associated relevance information as training data for a probabilistic retrieval model. A 1 entry indicates that the term occurs in a document, and 0 means it does not: R or NR indicate the relevance of the document with respect to queries in the training data. | T1 T2 T3 T4 T5 T6 Relevance DI TOT 11TOTOR D20 101 101TR 10 D4 0 1 11 D5 1 1 1 0 1 0 0 D6 1 1 0 1 1 1 D7 0 0 0 0 0 1 D8 0 0 1 1 1 0 1 D9 EEEEE T 1 27 D10 Using the basic probabilistic retrieval model, compute the relevance and non-relevance probabilities associated with terms T1 through T6 (show these probabilities in a table). Then, using these probabilities and a given query Q = (1,1,0,1,0,1), compute the discriminant Disc(Q, D11) and Disc(Q, D12) for each of the two new documents D11 and D12 given below: D11 = (0,1,1,0,0,1) o D12 = (1,0,1,1,0,1) Based on the discriminants, should these documents be retrieved? Explain your