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Introduction to Information Retrieval Information Retrieval and Data Mining (AT71.07) Comp. Sc. and Inf. Mgmt. Asian Institute of Technology Instructor:

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Presentation on theme: "Introduction to Information Retrieval Information Retrieval and Data Mining (AT71.07) Comp. Sc. and Inf. Mgmt. Asian Institute of Technology Instructor:"— Presentation transcript:

1 Introduction to Information Retrieval Information Retrieval and Data Mining (AT71.07) Comp. Sc. and Inf. Mgmt. Asian Institute of Technology Instructor: Dr. Sumanta Guha Slide Sources: Introduction to Information Retrieval book slides from Stanford University, adapted and supplemented Chapter 9: Relevance feedback and query expansion 1

2 Introduction to Information Retrieval Introduction to Information Retrieval CS276 Information Retrieval and Web Search Christopher Manning and Prabhakar Raghavan Lecture 9: Relevance feedback and query expansion

3 Introduction to Information Retrieval 3 Recap: Unranked retrieval evaluation: Precision and Recall Precision: fraction of retrieved docs that are relevant = P(relevant|retrieved) Recall: fraction of relevant docs that are retrieved = P(retrieved|relevant) Precision P = tp/(tp + fp) Recall R = tp/(tp + fn) RelevantNonrelevant Retrievedtpfp Not Retrievedfntn

4 Introduction to Information Retrieval 4 Recap: A combined measure: F Combined measure that assesses precision/recall tradeoff is F measure (weighted harmonic mean): where 2 = (1 - )/ People usually use balanced F 1 measure i.e., with = 1 or = ½ Harmonic mean is a conservative average See CJ van Rijsbergen, Information Retrieval

5 Introduction to Information Retrieval This lecture Improving results For high recall. E.g., searching for aircraft should match with plane; thermodynamics with heat Options for improving results… Global methods Query expansion Thesauri Automatic thesaurus generation Local methods Relevance feedback Pseudo relevance feedback

6 Introduction to Information Retrieval Relevance Feedback Relevance feedback: user feedback on relevance of docs in initial set of results User issues a (short, simple) query. System returns results. The user marks some results as relevant or non-relevant. The system computes a better representation of the information need based on feedback; returns results again. Repeat: Relevance feedback can go through one or more iterations. Principle: it may be difficult to formulate a good query when you dont know the collection well, so iterate Sec. 9.1

7 Introduction to Information Retrieval Relevance feedback We will use ad hoc retrieval to refer to regular retrieval without relevance feedback. We now look at four examples of relevance feedback that highlight different aspects. Sec. 9.1

8 Introduction to Information Retrieval Similar pages Clicking this gives feedback to the search engine that the doc is relevant!

9 Introduction to Information Retrieval Relevance Feedback: Example Image search engine Sec Site doesnt exist any more!

10 Introduction to Information Retrieval Results for Initial Query Sec

11 Introduction to Information Retrieval Relevance Feedback Sec

12 Introduction to Information Retrieval Results after Relevance Feedback Sec

13 Introduction to Information Retrieval Initial query/results Initial query: New space satellite applications , 08/13/91, NASA Hasnt Scrapped Imaging Spectrometer , 07/09/91, NASA Scratches Environment Gear From Satellite Plan , 04/04/90, Science Panel Backs NASA Satellite Plan, But Urges Launches of Smaller Probes , 09/09/91, A NASA Satellite Project Accomplishes Incredible Feat: Staying Within Budget , 07/24/90, Scientist Who Exposed Global Warming Proposes Satellites for Climate Research , 08/22/90, Report Provides Support for the Critics Of Using Big Satellites to Study Climate , 04/13/87, Arianespace Receives Satellite Launch Pact From Telesat Canada , 12/02/87, Telecommunications Tale of Two Companies User then marks relevant documents with Sec

14 Introduction to Information Retrieval Expanded query after relevance feedback new space satellite application nasa eos launch aster instrument arianespace bundespost ss rocket scientist broadcast earth oil measure Sec

15 Introduction to Information Retrieval Results for expanded query , 07/09/91, NASA Scratches Environment Gear From Satellite Plan , 08/13/91, NASA Hasnt Scrapped Imaging Spectrometer , 08/07/89, When the Pentagon Launches a Secret Satellite, Space Sleuths Do Some Spy Work of Their Own , 07/31/89, NASA Uses Warm Superconductors For Fast Circuit , 12/02/87, Telecommunications Tale of Two Companies , 07/09/91, Soviets May Adapt Parts of SS-20 Missile For Commercial Use , 07/12/88, Gaping Gap: Pentagon Lags in Race To Match the Soviets In Rocket Launchers , 06/14/90, Rescue of Satellite By Space Agency To Cost $90 Million * * * Sec

16 Introduction to Information Retrieval Key concept: Centroid The centroid is the center of mass of a set of points Recall that we represent documents as points in a high-dimensional space Definition: Centroid where C is a set of documents. Sec

17 Introduction to Information Retrieval Rocchio Algorithm The Rocchio algorithm uses the vector space model to pick a relevance fed-back query Given an initial query q 0, Rocchio seeks to manufacture the optimal query q opt that maximizes similarity with docs relevant to q 0 and minimizes similarity with docs non-relevant to q 0, i.e., Solution: Problem: we dont know the truly relevant docs! Sec

18 Introduction to Information Retrieval The Theoretically Best Query x x x x o o o Optimal query x non-relevant documents o relevant documents o o o x x x x x x x x x x x x x x Sec

19 Introduction to Information Retrieval Rocchio 1971 Algorithm (SMART) Used in practice: D r = set of known relevant doc vectors D nr = set of known irrelevant doc vectors Different from C r and C nr q m = modified query vector; q 0 = original query vector; α, β,γ: weights (hand-chosen or set empirically) New query moves toward relevant documents and away from irrelevant documents ! Sec

20 Introduction to Information Retrieval Subtleties to note Tradeoff α vs. β/γ : If we have a lot of judged documents, we want a higher β/γ. Some weights (= components) of new query vector q m can become negative because of its formula Negative term weights are ignored (set to 0) Sec

21 Introduction to Information Retrieval Relevance feedback on initial query x x x x o o o Revised query x known non-relevant documents o known relevant documents o o o x x x x x x x x x x x x x x Initial query Sec

22 Introduction to Information Retrieval Relevance Feedback in vector spaces We can modify the query based on relevance feedback and apply standard vector space model. Use only the docs that were marked. Relevance feedback can improve recall and precision Relevance feedback is most useful for increasing recall in situations where recall is important Assumption is that users will make effort to review results and to take time to iterate Sec

23 Introduction to Information Retrieval Positive vs Negative Feedback Positive feedback is more valuable than negative feedback (so, set < ; e.g. = 0.25, = 0.75). Many systems allow only positive feedback ( =0). Exercise 9.1: Under what conditions would the query q m in be the same as the query q 0 ? In all other cases, is q m closer than q 0 to the centroid of relevant docs? Exercise 9.2: Why is positive feedback likely to be more useful than negative feedback to an IR system? Sec

24 Introduction to Information Retrieval Probabilistic Relevance Feedback Build a Naive Bayes classifier using known relevant and known non-relevant docs as a training set: Recall Naive Bayes classifiers from DM and the example of a training set from a computer shop: Possible values for hypothesis H are C 1 :buys_computer = yes and C 2 :buys_computer = no Attributes X = (age, income, student, credit_rating) Select C i s.t. P(C i |X) is maximum. In case of doc classification: Possible values for hypothesis H are C 1 :doc_relevant = yes and C 2 :doc_relevant = no Attributes X = (term 1, term 2, …, term k ) Select C i s.t. P(C i |X) is maximum. Boolean attributes: present/not present in doc

25 Introduction to Information Retrieval Rocchio Relevance Feedback: Assumptions A1: User has sufficient knowledge for initial query. A2: Relevance prototypes are well-behaved. Term distribution in relevant documents will be similar Term distribution in non-relevant documents will be different from those in relevant documents In other words, relevant and non-relevant are bunched in separate clusters Sec x o o o o x x x o o o o

26 Introduction to Information Retrieval Relevance Feedback: Problems Long queries are inefficient for typical IR engine. Long response times for user. High cost for retrieval system. Partial solution: Only reweight certain prominent terms, e.g., top 20 by frequency Users in many domains are often reluctant to provide explicit feedback Empirically, one round of relevance feedback is often very useful. Two rounds is sometimes marginally useful. Domains where relevance feedback might be effective are recommender systems where a user is looking for a very specific answer, e.g., hotel, flight, etc.

27 Introduction to Information Retrieval Relevance Feedback on the Web Some search engines offer a similar/related pages feature (this is a trivial form of relevance feedback) Google (link-based) Altavista Stanford WebBase But some dont because its hard to explain to average user: Alltheweb bing Yahoo Excite initially had true relevance feedback, but abandoned it due to lack of use. Sec

28 Introduction to Information Retrieval Excite Relevance Feedback Spink et al Only about 4% of query sessions from a user used relevance feedback option Expressed as More like this link next to each result But about 70% of users only looked at first page of results and didnt pursue things further So 4% is about 1/8 of people extending search Relevance feedback improved results about 2/3 of the time Modern search engines have become extremely efficient at ad hoc retrieval (i.e., without relevance feedback), so importance of relevance feedback in search has diminished Sec

29 Introduction to Information Retrieval Pseudo relevance feedback Pseudo-relevance feedback automates the manual part of true relevance feedback. Pseudo-relevance algorithm: Retrieve a ranked list of hits for the users query Assume that the top k documents are relevant. Do relevance feedback (e.g., Rocchio) Works very well on average But can go horribly wrong for some queries. Several iterations can cause query drift Sec

30 Introduction to Information Retrieval Query Expansion In relevance feedback, users give additional input (relevant/non-relevant) on documents, which is used to reweight terms in the documents In query expansion, users give additional input (good/bad search term) on words or phrases Sec

31 Introduction to Information Retrieval Query assist (assisted expansion) Would you expect such a feature to increase the query volume at a search engine?

32 Introduction to Information Retrieval Global method: Query Reformulation Manual thesaurus E.g. MedLine: physician, syn: doc, doctor, MD, medico feline feline cat Global Analysis: (static; of all documents in collection) Automatically derived thesaurus (co-occurrence statistics) Refinements based on query log mining Common on the web Sec

33 Introduction to Information Retrieval Co-occurrence Thesaurus Simplest way to compute one is based on term-term similarities in C = AA T where A is term-document matrix. w i,j = (normalized) weight for (t i,d j ) For each t i, pick terms with high values in C titi djdj N M What does C contain if A is a term- doc incidence (0/1) matrix? Sec

34 Introduction to Information Retrieval Automatic Thesaurus Generation Example Sec


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