1. Consider the following two simple documents. [50 points] (A) precision is very very high (B) high precision is very very very important Assume the only stopwords are: is", am" and "are" in our system. 1) For each document, write down the normalized vector of term frequency of each term Compute the cosine using the format term: value, term: value, term: value, similarity of the two documents 2) Consider the tf-idf weighting. For each document, write down the normalized vector of 3) Consider the query "precision is high". Transfer the query into vector space by using 4) Now consider a vector space where each dimension is a word bigram instead of single tf-idf weights. Compute the cosine similarity of the two documents TF.IDF, then rank document A and B for given query using cosine similarity term. For each document, write down the normalized vector of bigram frequency {bigram: value, bigram: value, } . Compute the cosine similarity between documents A and B 5) For each document, write down the normalized vector of tf-idf weights where each dimension is a word bigram. Compute the cosine similarity between documents A and B