→ Yokuu : 圖資系已收 01/16 22:55
課程名稱︰資訊檢索
課程性質︰必修
課程教師︰唐牧群
開課學院：文學院
開課系所︰圖資系
考試日期（年月日）︰1030114
考試時限（分鐘）：100
試題 :
1. Here is an imaginary database that contains the following 5 document:
D1: "a dog barks at a cat and the dog fell"
D2: "a dog watches ants on the bark"
D3: "a dog watches a dog barks an ant"
D4: "a dog barks at a cat watches a cat"
D5: "an ant fell from the bark"
(Terms in the stop word list have been marked with grey).
Please
1a. Create an inverted file for the database where each cell contains the
TF*IDF weight of each term in the documents. When calculate IDF,
simply use N/n without logarithm)
1b. Calculate relevance scores and rank the documents accordingly after the
user submits the query"dog watch cat"
1c. After examining the results, the user marks D3, D4 as relevant, and
give no non-relevant. Produce the new ranking using Rocchio's
methodwhere α=1.0 β=1.0 γ=1.0
1d. With the same query and relevance information, calculate the new query
term weight for "dog","watch","cat"accrrding to their relevant odds and
Robertson and Spark Jones term weighting method.
2. Unlike data retrieval where perfect precision and recall are guaranteed,
information retrieval is more of a probabilistic process where information
conveyed in the retrieved documents might or might not answer user's
information needs. What are the possible causes behind the uncertainty of IR?
3. Define the following concepts and explain how they are related to one
another: "specificity", "precision" and "IDF (Inverse document Frequency);
"exhaustivity", "recall" and "TF(Term Frequency)".There is often a trade-off
between precision and recall, is there also a trade-off between specificity
and exhaustivity?why?
4. Explain the three basic models in information retrieve: Boolean, Vector
space and Probabilistic.(I expect you to explain the relevance of Bayes'
theorem in Probabilistic model)
5. Explain the rationale behind PageRank and the meaning of each component of
the formula below.
PR(A)=(1 - d) + d ΣPR(Ii) / C(Ii)
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