SECTION A 1.
The following documents have been processed by an IR system where stemming is not applied:
DocID Text
Doc1 breakthrough vaccine for covid19
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Doc2 new covid19 vaccine is approved
Doc3 new approach for treating patients
Doc4 new hopes for new covid19 patients in the world
(i) Assume that the following terms are stopwords: in, is, for, the. Construct an inverted file for these documents, showing clearly the dictionary and posting list components. Your inverted file needs to store sufficient information for computing a simple tf*idf term weight, where wij = tfij*log2(N/dfi)
Consider the recall-precision graph below showing the performances of two variants of a search engine that mimic Google Scholar on a collection of research papers. There is no difference between the two variants apart from how they score documents. Assume that you are a student looking to find all published papers on a
(ii) Compute the term weights of the two terms “breakthrough” and “vaccine” in Doc1. Show your working.
(iii) Assuming the use of a best match ranking algorithm, rank all documents using their relevance scores for the following query:
covid19 vaccine
Show your working. Note that log2(0.75)= -0.4150 and log2(1.3333)= 0.4150.
(iv) Typically, a log scale is applied to the tf (term frequency) component when scoring documents using a simple tf*idf term weighting scheme. Explain why this is the case illustrating your answer with a suitable example in IR. Explain through examples how models such as BM25 and PL2 control the term frequency counts.
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given topic. In other words, you do not want to miss any of the relevant documents. Explain which search engine will be more suitable for your task and why?
(c) Assume that you have decided to modify the approach you use to rank the documents of your collection. You have developed a new Web ranking approach that makes use of recent advances in neural networks. Explain in detail the steps you need to undertake to determine whether your new Web ranking approach produces a better retrieval performance than the original ranking approach.
(d) Consider a query with two terms, whose posting lists are as follows: term1 [id=2, tf=2], [id=5, tf=1], [id=6, tf=1]
term2 [id=2, tf=4], [id=4, tf=3] , [id=5, tf=4]
Explain and provide the exact steps/order in which the posting lists will be traversed by the TAAT & DAAT query evaluation strategies and the memory requirements of both strategies for obtaining a result set of K documents from a corpus of N documents (K
Assume another document doc2 in the corpus, which is identical to doc1 with the exception that one occurrence of w1 has been changed to word w5. Hence, we have ct(w1, doc2 ) = 1 and ct(w5, doc2) = 3.
Let q1 = w1 w5 be the new query.
If no smoothing is applied, using the query likelihood retrieval method, state which of the two documents (doc1 or doc2) will be ranked higher. Justify you answer.
Using the query likelihood retrieval method but this time with Dirichlet prior smoothing applied ( = 10), show which of the two documents (doc1 or doc2) would be ranked higher. Show your calculations.
Discuss whether smoothing has an impact on the ranking order of doc1 and doc2 and how? Justify your answer.
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SECTION B 3. (a)
Consider the following vector space scoring formula:
𝑆𝑐𝑜𝑒𝑑, 𝑐,∗𝑐,𝑑∗𝑁 1 ∈,∈ 𝑀 1
where ct(w,d) and ct(w, q) are the raw counts of word w in document d and query q, respectively (in other words, the term frequency of w in d and q, respectively); Nw is the number of documents in the corpus that contain word w, and M is the total number of documents in the corpus. Provide 4 reasons why the retrieval formula above is very unlikely to perform well in a Web search context. Justify your answers.
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For a particular query q, the multi-grade relevance judgements of all documents are {(d1,1),(d3, 4),(d6, 2),(d9, 3),(d11, 1),(d31, 2)}, where each tuple represents a document ID and a relevance judgment pair, and all the other documents are judged as non-relevant. Documents are judged on the scale 0-4 (0:not relevant 4:highly relevant). Two IR systems return their retrieval results with respect to this query as follows (these are all results they have returned for this query):
System A: {d1, d2, d3, d4, d5, d6, d7} System B: {d31, d22, d3, d6, d15}
For both System A and System B, compute the following ranking evaluation metrics. You must clearly articulate how you compute each of these metrics. Since there are two DCG definitions discussed in the class, you should use the original one where 1/log2 (rank) is used as the discount factor that is applied to the gain:
Average Precision (AP). Show your calculations.
Normalised Discounted Cumulative Gain (NDCG) for each rank position. In your answer, provide the ideal DCG values for the perfect ranking for the given query.Youmightwishtonotethatlog2 2=1;log2 3=1.59;log2 4=2;log2 5 = 2.32; log2 6 = 2.59 and log2 7 = 2.81. Show your calculations.
(c) URL length has been shown to be an important feature for some Web search tasks. Discuss which types of information needs on the Web, the URL length feature is most appropriate for.
Consider a linear learning to rank model for Web search using 4 features: PL2, Proximity, PageRank and URL length. Using such a model, explain the main disadvantage of using linear learning to rank models in Web search.
[5] (d) A posting list for a term in an inverted index contains the following three entries:
id=3 tf=4 id=7 tf=3 id=10 tf=5
Applying the delta compression of ids, show the binary form of the unary compressed posting list. What is the resulting (mean) compression rate, in bits per integer?
(e) A Web search engine has devised a new interface to present its search results. Describe three specific approaches that could be used by the search engine to evaluate the interface change.
Which approach you would recommend and why?
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