UIUC University Coursera Course text retrieval and Search Engines:week 1 Practice University

Week 1 Practice quizhelp Center

Warning:the hard deadline has passed. You can attempt it, but and you won't be. You are are welcome to try it as a learning exercise. In accordance with the Coursera Honor Code, I certify this answers here are I own work. Question 1 Consider the instantiation of the vector space model where documents and queries are represented as term Ency vectors. Assume we have the following query and two documents:

Q = "Future of online education"
D1 = "Coursera is shaping" future of online education; Online education is affordable. ”
D2 = "In the future, online education'll dominate."

Let V (X) = [C1 c2 c3 C4] represent a part of term frequency vector for document or query X, where C1, C2, C3, and C4 Are the term weights corresponding to "future", "of", "online", and "education", respectively.   Which of the following is True:v (q) = [1 1 1 1] V (D1) = [1 1 2 2] V (D2) = [1 1 1] V (q) = [1 1 1 1] V (D1) = [1 1 1 2] V (D2) = [1 0 1 1] V (Q) = [1 1 1 1] V (D1) = [1 1, 1 1] V (D2) = [1 0 1 1] Question 2 Consider the same scenario as in que Stion (1) with the dot product as the similarity measure. Which of the following is the True:sim (q,d1) = 6 Sim (Q,D2) = 3 Sim (Q,D1) = 4 Sim (Q,D2) = 3 Sim (q,d1) = 6 Sim (Q,D2) = 4 Question 3 Assume we have two documents with the same raw TF to all of the query words (i.e. the query words appear with th E same frequency in both documents). Then, using the OKAPI/BM25 retrieval function, the longer document would have a lower score. False True Question 4 If We remove the document length normalization term from the OKAPI/BM25 retrieval function, and have two documents with the same raw TF to all the query words, then the longer document would have a higher SCO Re. True False