3. Question 3 You are given the query Q= “online courses” and two documents: D1 = “online courses search engine” D2 = “online education is affordable” Assume you are using…
2. Question 2 You are given a vocabulary composed of only four words: “the,” “computer,” “science,” and “technology.” Below are the probabilities of three of these four words given by…
8. Question 8 Refer to the Rocchio feedback formula in the lectures. If you want to eliminate the effect of non-relevant documents when doing feedback, which of the following parameters…
10. Question 10 Which of the following is not true about the KL-divergence retrieval model? 1 point It supports relevance feedback. It represents both queries and documents as…
7. Question 7 It is possible that pseudo feedback decreases the precision and recall of a certain retrieval system. 1 point True False
1. Question 1 Assume you are using a unigram language model to calculate the probabilities of phrases. Then, the probabilities of generating the phrases “study text mining” and “text mining…
9. Question 9 Let qq be the original query vector, D_R=\{P_1,…,P_n \}DR={P1,…,Pn} be the set of positive document vectors, and D_N=\{N_1,…,N_m\}DN={N1,…,Nm} be the set of negative document vectors. Let q_1q1…
6. Question 6 Assume you are using Dirichlet Prior smoothing to estimate the probabilities of words in a certain document. What happens to the smoothed probability of the word when…
5. Question 5 If word count for every term doubles in one document: 1 point If not using any smoothing, query likelihood would change for some queries. p(w…
4. Question 4 Assume the same scenario as in Question 3, but using linear interpolation (Jelinek-Mercer) smoothing with \lambda = 0.5λ=0.5. Furthermore, you are given the following probabilities of some…
