Module Contact: Dr Dan Smith, CMP Copyright of the University of East Anglia Version 1

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1 UNIVERSITY OF EAST ANGLIA School of Computing Sciences Main Series UG Examination 2015/16 INFORMATION RETRIEVAL CMP-5036A/CMP-6008A Time allowed: 2 hours Answer any TWO questions. Notes are not permitted in this examination. Do not turn over until you are told to do so by the Invigilator. CMP-5036A/CMP-6008A Module Contact: Dr Dan Smith, CMP Copyright of the University of East Anglia Version 1

2 Page 2 1. (a) Describe the Rocchio technique for relevance feedback in information retrieval. Use appropriate mathematical expressions, and make sure that every term you use is clearly defined. What is the main problem with this technique and what practical measures can be taken to alleviate this problem? [15 marks] (b) What is query expansion in information retrieval? Describe two techniques for query expansion and comment on any problems associated with them. [10 marks] (c) Explain why n-grams of words are useful in information retrieval, using examples that demonstrate this. Despite their usefulness, search engines often do not use them extensively. Why? [5 marks] (d) Explain why direct estimation of the probability of observing a phrase or sentence drawn from a particular natural language is unfeasible. Explain how the n-gram assumption circumvents this problem. Use the mathematical expressions appropriate for bigrams in your explanation. [10 marks] (e) A hypothetical language has only three words, A, B, and C. An example of a sentence from the language is given below: C C A B B A B B C C A B B A A. Using this sentence, obtain unsmoothed estimates of (i) the unigram probabilities of the words in the language; (ii) the observed bigram probabilities. [3 marks] [7 marks] (f) Find the probability of observing the sentence B C A B using your estimated unigram and bigram probabilities. [5 marks] (g) Explain what problem occurs when estimating the probability of the sentence C B C using bigrams. How can this problem be addressed? [5 marks] CMP-5036A/CMP-6008A Version 1

3 Page 3 2. (a) Describe the processes by which a user s information need is communicated to a typical search engine, a response generated, and the user s possible reactions to the system s response. (b) Describe the main components of a simple inverted index suitable for bag of words information retrieval methods. You should illustrate your answer with diagrams, where appropriate. (c) Describe how this structure can be extended to accommodate proximity search and the effects these changes are likely to have on index size and search time. [16 marks] (d) Describe the basic structure and main operations of a simple web crawler, how these are enhanced in current commercial crawlers, and discuss the issues faced in ensuring that crawlers are directed towards good quality material.you should illustrate your answer with diagrams as necessary. [20 marks] CMP-5036A/CMP-6008A Version 1 TURN OVER

4 Page 4 3. (a) State, in your own words, the Probability Ranking Principle for Information Retrieval. (b) Suppose a document is represented as a vector of terms x, and it can be either relevant (R) or non-relevant (NR) to a certain query, represented as a vector q. It can be shown that the likelihood ratio L = Pr(R q, x) Pr(NR q, x) (1) can be written as L = Pr( x R, q) Pr( x NR, q) Pr(R q) Pr(NR q). (2) (i) Explain the meaning of both the numerator and denominator of the first term in equation 2. How can this term be interpreted in terms of odds? (ii) Why can the second term of equation 2 be ignored when ranking a set of documents with respect to their relevance to the query? [4 marks] (c) Suppose that we have a set of 10 documents some of which we know are relevant (R) to a certain query and some of which are non-relevant (NR). We also know whether the first term of the query, x 1, appears in each of the documents. This information is shown in Table 1. Table 1: Relevance/non-relevance of 10 documents to a query, and presence (1) or absence (0) of term x 1 in these documents. Document No: Presence: Relevance: R R R R R R NR NR NR NR Let p 1 be the probability that term x 1 is present in a relevant document and u 1 the prob. that term x 1 is present in a non-relevant CMP-5036A/CMP-6008A Version 1

5 Page 5 document. (i) Make estimates of p 1 and u 1. (ii) The quality of a term x i for retrieval can be estimated by computing c i, where c i = log p i (1 p i ) + log (1 u i). (3) u i (A) How can this term be interpreted in terms of odds? (B) Explain why a problem is caused if the term x i does not appear in any of the documents known to be relevant or non-relevant. How can this problem be alleviated? (C) Show that, by making a reasonable assumptions about the proportion of relevant documents in a large collection, the second term in equation 3 can be estimated as log(n/d f i ), where N is the number of documents in the collection and d f i is the document frequency i.e. the number of documents that the term x i appears in. State clearly the assumptions you make, and comment on the significance of this result. CMP-5036A/CMP-6008A Version 1 TURN OVER

6 Page 6 4. (a) Explain (i) the development of link analysis techniques in bibliometrics, (ii) the motivation for the use of link analysis algorithms in web search, and (iii) the terms authority, hub, and in-degree for link analysis algorithms. (b) Describe the main recent developments in search engine technology designed to improve the quality of search results. (c) Describe the simple version of the PageRank algorithm, using relevant diagrams and formulae in your answer, and briefly explain its importance in the success of Google s search engine. [24 marks] (d) Discuss the most important aspects of web documents that are used as signals of good (or bad) quality by search engines. [16 marks] END OF PAPER CMP-5036A/CMP-6008A Version 1