The following text corpus consists of 7 documents. Refer the following document term matrix Term tl t2 t3 d5 0 0 d6 0 0 d1 2 1 3 0 0 0 d2 1 2. 1 0 0 0 d3 0 0 0 1 1 1 Document d4 0 0 0 2 1 1 dz 0 1 0 1 1 0 t5 t6 1 1 0 1 1 0 The documents were vectorized and the frequency of each term was recording (see fig. below). You decided to create two new documents out of the old ones by combining d1, d2 and d7 into D1, d3, d4, d5, d6 into D2. a) Compute the word frequencies for D1 and D2 based on the bag-of-words model. Compute the cosine similarity between D1 and D2. b) Update the word frequencies for D1 and D2 based on the one-hot encoding model. Compute the cosine similarity between D1 and D2. The following text corpus consists of 7 documents. Refer the following document term matrix Term tl t2 t3 d5 0 0 d6 0 0 d1 2 1 3 0 0 0 d2 1 2. 1 0 0 0 d3 0 0 0 1 1 1 Document d4 0 0 0 2 1 1 dz 0 1 0 1 1 0 t5 t6 1 1 0 1 1 0 The documents were vectorized and the frequency of each term was recording (see fig. below). You decided to create two new documents out of the old ones by combining d1, d2 and d7 into D1, d3, d4, d5, d6 into D2. a) Compute the word frequencies for D1 and D2 based on the bag-of-words model. Compute the cosine similarity between D1 and D2. b) Update the word frequencies for D1 and D2 based on the one-hot encoding model. Compute the cosine similarity between D1 and D2