Introduction to Information Retrieval

Introduction to Information Retrieval Introduction to Information Retrieval Lecture 9 Introduction to Information Retrieval Recap of the last lecture  Evaluating a search engine  Benchmarks  Precision and recall  Results summaries Introduction to Information 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) Relevant Nonrelevant Retrieved tp fp Not Retrieved fn tn  Precision P = tp/(tp + fp)  Recall R = tp/(tp + fn) 3 Introduction to Information Retrieval Recap: A combined measure: F  Combined measure that assesses precision/recall tradeoff is F measure (weighted harmonic mean): 1 2 (   1) PR F  2 1 1  PR   (1   ) P R  People usually use balanced F1 measure  i.e., with  = 1 or  = ½  Harmonic mean is a conservative average  See CJ van Rijsbergen, Information Retrieval 4 Introduction to Information Retrieval This lecture  Improving results  For high recall. E.g., searching for aircraft doesn’t match with plane; nor thermodynamic with heat  Options for improving results…  Global methods  Query expansion  Thesauri  Automatic thesaurus generation  Local methods  Relevance feedback  Pseudo relevance feedback Introduction to Information Retrieval Sec. 9.1 Relevance Feedback  Relevance feedback: user feedback on relevance of docs in initial set of results  User issues a (short, simple) query  The user marks some results as relevant or nonrelevant.  The system computes a better representation of the information need based on feedback.  Relevance feedback can go through one or more iterations.  Idea: it may be difficult to formulate a good query when you don’t know the collection well, so iterate Introduction to Information Retrieval Sec. 9.1 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. Introduction to Information Retrieval Similar pages Introduction to Information Retrieval Sec. 9.1.1 Relevance Feedback: Example  Image search engine http://nayana.ece.ucsb.edu/imsearch/imsearch.h tml Introduction to Information Retrieval Results for Initial Query Sec. 9.1.1 Introduction to Information Retrieval Relevance Feedback Sec. 9.1.1 Introduction to Information Retrieval Results after Relevance Feedback Sec. 9.1.1 Introduction to Information Retrieval Ad hoc results for query canine Ad hoc results for query canine Introduction to Information Retrieval Introduction to Information Retrieval User feedback: Select what is relevant Results after relevance feedback Introduction to Information Retrieval Introduction to Information Retrieval Sec. 9.1.1 Initial query/results  Initial query: New space satellite applications + 1. 0.539, 08/13/91, NASA Hasn’t Scrapped Imaging Spectrometer + 2. 0.533, 07/09/91, NASA Scratches Environment Gear From Satellite Plan + 3. 0.528, 04/04/90, Science Panel Backs NASA Satellite Plan, But Urges Launches of Smaller Probes 4. 0.526, 09/09/91, A NASA Satellite Project Accomplishes Incredible Feat: Staying Within Budget 5. 0.525, 07/24/90, Scientist Who Exposed Global Warming Proposes Satellites for Climate Research 6. 0.524, 08/22/90, Report Provides Support for the Critics Of Using Big Satellites to Study Climate 7. 0.516, 04/13/87, Arianespace Receives Satellite Launch Pact From Telesat Canada 8. 0.509, 12/02/87, Telecommunications Tale of Two Companies  User then marks relevant documents with “+”. Introduction to Information Retrieval Expanded query after relevance feedback          2.074 new 15.106 space 30.816 satellite 5.660 application 5.991 nasa 5.196 eos 4.196 launch 3.972 aster 3.516 instrument 3.446 arianespace 3.004 bundespost 2.806 ss 2.790 rocket 2.053 scientist 2.003 broadcast 1.172 earth 0.836 oil 0.646 measure Sec. 9.1.1 Introduction to Information Retrieval Sec. 9.1.1 Results for expanded query 2 1. 0.513, 07/09/91, NASA Scratches Environment Gear From Satellite Plan 1 2. 0.500, 08/13/91, NASA Hasn’t Scrapped Imaging Spectrometer 3. 0.493, 08/07/89, When the Pentagon Launches a Secret Satellite, Space Sleuths Do Some Spy Work of Their Own 8 4. 0.493, 07/31/89, NASA Uses ‘Warm’ Superconductors For Fast Circuit 5. 0.492, 12/02/87, Telecommunications Tale of Two Companies 6. 0.491, 07/09/91, Soviets May Adapt Parts of SS-20 Missile For Commercial Use 7. 0.490, 07/12/88, Gaping Gap: Pentagon Lags in Race To Match the Soviets In Rocket Launchers 8. 0.490, 06/14/90, Rescue of Satellite By Space Agency To Cost $90 Million Introduction to Information Retrieval Sec. 9.1.1 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   1  Definition: Centroid  (C )  d  |C | d C where C is a set of documents. Introduction to Information Retrieval Sec. 9.1.1 Rocchio Algorithm  The Rocchio algorithm uses the vector space model to pick a relevance feedback query  Rocchio seeks the query qopt that maximizes      qopt arg max [cos( q ,  (Cr ))  cos( q ,  (Cnr ))]  q  Tries to separate docs marked relevant and nonrelevant  1 qopt  Cr  1 d   j  Cnr d j Cr  d  j  d j Cr  Problem: we don’t know the truly relevant docs Introduction to Information Retrieval Sec. 9.1.1 The Theoretically Best Query x x o x x o o  o o o x Optimal query x x x x x x x x x x x x x x non-relevant documents o relevant documents Introduction to Information Retrieval Sec. 9.1.1 Rocchio 1971 Algorithm (SMART)  Used in practice:   1 qm q0   Dr  1 d    j  Dnr d j Dr  d  j  d j Dnr  Dr = set of known relevant doc vectors  Dnr = set of known irrelevant doc vectors  Different from Cr and Cnr! Q = query, Cr = relevant documents Cnr = not relevant documents  qm = modified query vector; q0 = original query vector; α,β,γ: weights (hand-chosen or set empirically)  New query moves toward relevant documents and away from irrelevant documents Introduction to Information Retrieval Sec. 9.1.1 Subtleties to note  Tradeoff α vs. β/γ : If we have a lot of judged documents, we want a higher β/γ.  Some weights in query vector can go negative  Negative term weights are ignored (set to 0) Introduction to Information Retrieval Sec. 9.1.1 Relevance feedback on initial query Initial query x o x  o x x x Revised query x x o o o x x x x x x x x o x x x x known non-relevant documents o known relevant documents Introduction to Information Retrieval Sec. 9.1.1 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  Users can be expected to review results and to take time to iterate Introduction to Information Retrieval Sec. 9.1.1 Positive vs Negative Feedback  Positive feedback is more valuable than negative feedback (so, set  < ; e.g.  = 0.25,  = 0.75).  Many systems only allow positive feedback (=0). y? h W Introduction to Information Retrieval Aside: Vector Space can be Counterintuitive. Snow document puts a lot of Doc “J. Snow & Cholera” x x Query “cholera” weird historical stuff into the vector x x x x x x x o x x x x x q1 x x x x x x x x q1 query “cholera” o www.ph.ucla.edu/epi/snow.html x other documents Introduction to Information Retrieval High-dimensional Vector Spaces  The queries “cholera” and “john snow” are far from each other in vector space.  How can the document “John Snow and Cholera” be close to both of them?  Our intuitions for 2- and 3-dimensional space don't work in >10,000 dimensions.  3 dimensions: If a document is close to many queries, then some of these queries must be close to each other.  Doesn't hold for a high-dimensional Introduction to Information Retrieval Sec. 9.1.3 Relevance Feedback: Assumptions  A1: User has sufficient knowledge for initial query.  A2: Relevance prototypes are “wellbehaved”.  Term distribution in relevant documents will be similar  Term distribution in non-relevant documents will be different from those in relevant documents Introduction to Information Retrieval Sec. 9.1.3 Violation of A1  User does not have sufficient initial knowledge.  Examples:  Misspellings (Brittany Speers).  Cross-language information retrieval (hígado).  Mismatch of searcher’s vocabulary vs. collection vocabulary  Cosmonaut/astronaut Introduction to Information Retrieval Sec. 9.1.3 Violation of A2  There are several relevance prototypes.  Examples:  Burma/Myanmar  Contradictory government policies  Pop stars that worked at Burger King  Often: instances of a general concept  Good editorial content can address problem  Report on contradictory government policies 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: W hy ?  Only reweight certain prominent terms  Perhaps top 20 by term frequency  Users are often unwilling to provide explicit feedback  It’s often harder to understand why a particular document was retrieved after applying relevance feedback Introduction to Information Retrieval Evaluation of relevance feedback strategies Sec. 9.1.5  Use q0 and compute precision and recall graph  Use qm and compute precision recall graph  Assess on all documents in the collection  Spectacular improvements, but … it’s cheating!  Partly due to known relevant documents ranked higher  Must evaluate with respect to documents not seen by user  Use documents in residual collection (set of documents minus those assessed relevant)  Measures usually then lower than for original query  But a more realistic evaluation  Relative performance can be validly compared  Empirically, one round of relevant feedback is often very useful. Two rounds is sometimes marginally useful. Introduction to Information Retrieval Evaluation of relevance feedback Sec. 9.1.5  Second method – assess only the docs not rated by the user in the first round  Could make relevance feedback look worse than it really is  Can still assess relative performance of algorithms  Most satisfactory – use two collections each with their own relevant assessments  Q0 and user feedback from the first collection  Qm run on the second collection and measured Introduction to Information Retrieval Sec. 9.1.3 Evaluation: Caveat  True evaluation of usefulness must compare to other methods taking the same amount of time.  Alternative to relevance feedback: User revises and resubmits query.  Users may prefer revision/resubmission to having to judge relevance of documents.  There is no clear evidence that relevant feedback is the “best use” of the user’s time. Introduction to Information Retrieval Sec. 9.1.4 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 α/β/γ ??  But some don’t because it’s hard to explain to average user:  Alltheweb  bing  Yahoo  Excite initially had true relevance feedback, but abandoned it due to lack of use. Introduction to Information Retrieval Sec. 9.1.4 Excite Relevance Feedback Spink et al. 2000  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 the first page of results and didn’t pursue things further  So 4% is about 1/8 of people extending search  Relevance feedback improved results about 2/3 of the time Introduction to Information Retrieval Pseudo relevance feedback Pseudo-relevance feedback automates the “manual” part of true relevance feedback. 39 Introduction to Information Retrieval Sec. 9.1.6 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 user’s query  Assume that the top k documents are relevant.  Do relevant feedback (e.g., Rocchio)  Works very well on average  But can go horribly wrong for some queries.  Several iterations can cause query drift.  Why? Introduction to Information Retrieval Sec. 9.2.2 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 Introduction to Information Retrieval Query assist Would you expect such a feature to increase the query volume at a search engine? Introduction to Information Retrieval Sec. 9.2.2 How do we augment the user query?  Manual thesaurus  E.g. MedLine: physician, syn: doc, doctor, MD, medico  Can be query rather than just synonyms  Global Analysis: (static; of all documents in collection)  Automatically derived thesaurus  (co-occurrence statistics)  Refinements based on query log mining  Common on the web  Local Analysis: (dynamic)  Analysis of documents in result set Introduction to Information Retrieval Sec. 9.2.2 Example of manual thesaurus Introduction to Information Retrieval Thesaurus-based query expansion Sec. 9.2.2  For each term, t, in a query, expand the query with synonyms and related words of t from the thesaurus  feline → feline cat  May weight added terms less than original query terms.  Generally increases recall  Widely used in many science/engineering fields  May significantly decrease precision, particularly with ambiguous terms.  “interest rate”  “interest rate fascinate evaluate”  There is a high cost of manually producing a thesaurus  And for updating it for scientific changes Introduction to Information Retrieval Automatic Thesaurus Generation Sec. 9.2.3  Attempt to generate a thesaurus automatically by analyzing the collection of documents  Fundamental notion: similarity between two words  Definition 1: Two words are similar if they co-occur with similar words.  Definition 2: Two words are similar if they occur in a given grammatical relation with the same words.  You can harvest, peel, eat, prepare, etc. apples and pears, so apples and pears must be similar.  Co-occurrence-based is more robust, and grammatical relations are more accurate. Introduction to Information Retrieval Sec. 9.2.3 Co-occurrence Thesaurus  Simplest way to compute one is based on term-term similarities in C = AAT where A is term-document matrix.  wi,j = (normalized) weight for (ti ,dj)  For each ti, pick terms with high values in C ti M dj N What does C contain if A is a termdoc incidence (0/1) matrix? Introduction to Information Retrieval Sec. 9.2.3 Automatic Thesaurus Generation Example Introduction to Information Retrieval Sec. 9.2.3 Automatic Thesaurus Generation Discussion   Quality of associations is usually a problem. Term ambiguity may introduce irrelevant statistically correlated terms.    “Apple computer”  “Apple red fruit computer” Problems:  False positives: Words deemed similar that are not  False negatives: Words deemed dissimilar that are similar Since terms are highly correlated anyway, expansion may not retrieve many additional documents. Introduction to Information Retrieval Indirect relevance feedback  On the web, DirectHit introduced a form of indirect relevance feedback.  DirectHit ranked documents higher, which users look at more often.  Clicked-on links are assumed likely to be relevant  Assuming the displayed summaries are good, etc.  Globally: Not necessarily user or queryspecific.  This is the general area of clickstream mining  Today – handled as part of machine-learned ranking Introduction to Information Retrieval Resources IIR Ch 9 MG Ch. 4.7 MIR Ch. 5.2 – 5.4