2. There are 5 different documents in the collection: D1 = "If it walks like a duck and quacks like a duck, it must be a duck." D2 = "Beijing Duck is mostly prized for the thin, crispy duck skin." D3...

Extracted text: 2. There are 5 different documents in the collection: D1 = "If it walks like a duck and quacks like a duck, it must be a duck." D2 = "Beijing Duck is mostly prized for the thin, crispy duck skin." D3 = "Bugs' ascension to stardom also prompted the Warner animators to recast Daffy Duck as the rabbit's rival, intensely jealous and determined to steal back the spotlight while Bugs remained indifferent to the duck's jealousy, or used it to his advantage. This turned out to be the recipe for the success of the duo." D4 = "6:25 PM 1/7/2018 blog entry: I found this great recipe for Rabbit Braised in Wine on cookingforengineers.com." D5 = "Last week Li has shown you how to make the Sichuan duck. Today we'll be making Chinese dumplings (Jiaozi), a popular dish that I had a chance to try last summer in Beijing. There are many recipies for Jiaozi." (a) For the query Q = "Beijing duck recipe", find the two top ranked documents %3D according to the tf-idf rank. Assume the cosine similarity measure and the culinary term set T = {Beijing, dish, duck, rabbit, recipe, roast} (i.e. other terms are not indexed and thus not represented in the document vectors). Are the top ranked documents relevant to the query?