[試題] 100上 唐牧群 資訊檢索 期末考
課程名稱︰資訊檢索
課程性質︰圖資系大三必修
課程教師︰唐牧群
開課學院：文學院
開課系所︰圖書資訊學系
考試日期（年月日）︰101/01/10
考試時限（分鐘）：1.20~4.20
是否需發放獎勵金：要！
（如未明確表示，則不予發放）
試題 :
1. With an imaginary database that contains only the following 5 document:
(20 points)
D1:"a dog barks at a cat and a dog in a tree"
D2:"a dog watches ants eat the bark of a tree"
D3:"a dog watches another dog by a tree"
D4:"a dog barks at a cat on a tree"
D5:"the bark fell from the tree as a cat watches"
(Terms in the stop word list have been marked with lighter hue).
Please
1. Create an inverted file for the database where each cell contains the TF
(Term Frequency)weight of each term all the documents.
2.Calculate document frequecy(DF) and IDF weight for each index term(simply
use N/n without logarithm).
3. Give the ranking after the user submits the query"dog barks cat"
4. After the first iteration, the user examines the results and marks D1, D4
as relevant, and D2 and D5 as non-relevant. Produce the new ranking using
Rocchio's method where α=1.0 β=1.0 γ=1.0
Answer 4 out of the following 5 questions; each will acount for 20 points.
2. Unlike data retrieval, where perfect precision and recall are guaranted,
information retrieval is more of a probabilistic process where information
conveyed in the retrieved documents might or might not answer users'
information needs. What are the possible causes behind the uncertainty of IR?
3. Define the following concepts and explain hoe they are related to one
another:"specificity", "precision" and "IDF(Inverse document Frecuency);
"eshaustivity", "recall" and "TF(Term Frequency)".There is often a trade-off
between presicion and recall, is there also a trade-off between specificity
and exhaustivity?
4. Explain three basic models in information retrieve:Boolean, Vector space
Probabilistic.
5. Explain the rationales behind eliciting users' relevance feedback and how it
can improve search results. What are two mechanisms with which relevant terms
can be identified and extracted(hint: IQE and AQE)?
6. How does retrievel on the Web differ from retrieval with traditional
bibliographic databases(e.g the nature of Web document and Web environment,
the"structuredness" of indexing, and the use of link data etc.)? Give the
formula of Google's PageRank and explain its rationale.
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