Can someone help me with these questions? Note: Explanation is optional and I have provided an image.

Bag of words (BOW) and distances 1. Given a dictionary { \"capital\": 1, \"state\": 2, \"team\": 3, \"basketball\": 4, \"hockey\": 5, \"professional\": 6, \"bank\": 7}, derive the BOW representation (i.e., a 7-dimensional vector) for the following three sentences without normalization: a. A: [\"sacramento\"' \"is \"a\" \"state\" \"capital\" \"and\" \"it\" \"has\" \"a\" \"professional\" \"basketball\" \"team\"] b. B: [\"Columbus\" \"is\" \"a\" \"state\" \"capital\" \"and\" \"it\" \"has\" \"a\" \"professional\" \"hockey\" \"team\" \"but\" \"no\" \"professional\" \"basketball\" \"team\"] 0. C: [\"the\" \"capital\" \"bank\" \"at\" \"Cincinnati\" \"has\" \"a\" \"professional\" \"team\"] Note that, if a word shows up multiple times in the sentence, you should count it multiple times in the BOW representation. In other words, the representation can contain numbers that are not binary. NOTE: the questions below build on the responses from a,b,c. These questions build on each other basically. L1 distance: Compute the L1 distance between A and B; compute the L1 distance between A and C. L1 normalization: Perform L1 normalization to the BOW representations of A, B, and C first, and then compute the L1 distance between A and B, and between A and C