1. 1 CS 430 / INFO 430 Information Retrieval Lecture 8 Query Refinement and Relevance Feedback

2. 2 Course Administration Assignment Reports A sample report will be posted before the next assignment is due. Preparation for Discussion Classes Most of the readings were used last year. You can see the questions that were used on last year's Web site: http://www.cs.cornell.edu/Courses/cs430/2004fa/

3. 3 CS 430 / INFO 430 Information Retrieval Completion of Lecture 7

4. 4 Search for Substring In some information retrieval applications, any substring can be a search term. Tries, using suffix trees, provide lexicographical indexes for all the substrings in a document or set of documents.

5. 5 Tries: Search for Substring Basic concept The text is divided into unique semi-infinite strings, or sistrings. Each sistring has a starting position in the text, and continues to the right until it is unique. The sistrings are stored in (the leaves of) a tree, the suffix tree. Common parts are stored only once. Each sistring can be associated with a location within a document where the sistring occurs. Subtrees below a certain node represent all occurrences of the substring represented by that node. Suffix trees have a size of the same order of magnitude as the input documents.

6. 6 Tries: Suffix Tree Example: suffix tree for the following words: begin beginning between bread break b e rea gin tween d k null ning

7. 7 Tries: Sistrings A binary example String:01 100 100 010 111 Sistrings:101 100 100 010 111 211 001 000 101 11 310 010 001 011 1 400 100 010 111 501 000 101 11 610 001 011 1 700 010 111 800 101 11

8. 8 Tries: Lexical Ordering 700 010 111 400 100 010 111 800 101 11 501 000 101 11 101 100 100 010 111 610 001 011 1 310 010 001 011 1 211 001 000 101 11 Unique string indicated in blue

9. 9 Trie: Basic Concept 7 48 51 2 63 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1

10. 10 Patricia Tree 7 48 51 2 63 0 0 0 00 0 0 0 1 1 1 1 10 1 1 1223345 Single-descendant nodes are eliminated. Nodes have bit number.

11. 11 CS 430 / INFO 430 Information Retrieval Lecture 8 Query Refinement and Relevance Feedback

12. 12 Query Refinement Search Reformulate query Display retrieved information new query reformulated query Query formulation EXIT

13. 13 Reformulation of Query Manual Add or remove search terms Change Boolean operators Change wild cards Automatic Remove search terms Change weighting of search terms Add new search terms

14. 14 Manual Reformulation: Vocabulary Tools Feedback Information about stop lists, stemming, etc. Numbers of hits on each term or phrase Suggestions Thesaurus Browse lists of terms in the inverted index Controlled vocabulary

15. 15 Manual Reformulation: Document Tools Feedback to user consists of document excerpts or surrogates Shows the user how the system has interpreted the query Effective at suggesting how to restrict a search Shows examples of false hits Less good at suggesting how to expand a search No examples of missed items

16. 16 Relevance Feedback: Document Vectors as Points on a Surface Normalize all document vectors to be of length 1 Then the ends of the vectors all lie on a surface with unit radius For similar documents, we can represent parts of this surface as a flat region Similar document are represented as points that are close together on this surface

17. 17 Results of a Search x x x x x x x  hits from search x documents found by search  query

18. 18 Relevance Feedback (Concept) x x x x o o o  hits from original search x documents identified by user as non-relevant o documents identified by user as relevant  original query reformulated query

19. 19 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

20. 20 Theoretically Best Query For a specific query, q, let: D R be the set of all relevant documents D N-R be the set of all non-relevant documents sim (q, D R ) be the mean similarity between query q and documents in D R sim (q, D N-R ) be the mean similarity between query q and documents in D N-R The theoretically best query would maximize: F = sim (q, D R ) - sim (q, D N-R )

21. 21 Estimating the Best Query In practice, D R and D N-R are not known. (The objective is to find them.) However, the results of an initial query can be used to estimate sim (q, D R ) and sim (q, D N-R ).

22. 22 Rocchio's Modified Query Modified query vector = Original query vector + Mean of relevant documents found by original query - Mean of non-relevant documents found by original query

23. 23 Query Modification q 1 = q 0 + r i - s i  i =1 n1n1 n1n1 1  n2n2 n2n2 1 q 0 = vector for the initial query q 1 = vector for the modified query r i = vector for relevant document i s i = vector for non-relevant document i n 1 = number of relevant documents n 2 = number of non-relevant documents Rocchio 1971

24. 24 Difficulties with Relevance Feedback x x x x o o o  optimal query x non-relevant documents o relevant documents  original query reformulated query o o o x x x x x x x x x x x x x x Hits from the initial query are contained in the gray shaded area

25. 25 Difficulties with Relevance Feedback x x x x o o o  optimal results set x non-relevant documents o relevant documents  original query reformulated query o o o x x x x x x x x x x x x x x What region provides the optimal results set?

26. 26 Effectiveness of Relevance Feedback Best when: Relevant documents are tightly clustered (similarities are large) Similarities between relevant and non-relevant documents are small

27. 27 When to Use Relevance Feedback Relevance feedback is most important when the user wishes to increase recall, i.e., it is important to find all relevant documents. Under these circumstances, users can be expected to put effort into searching: Formulate queries thoughtfully with many terms Review results carefully to provide feedback Iterate several times Combine automatic query enhancement with studies of thesauruses and other manual enhancements

28. 28 Adjusting Parameters 1: Relevance Feedback q 1 =  q 0 +  r i -  s i  i =1 n1n1 n1n1 1  n2n2 n2n2 1 ,  and  are weights that adjust the importance of the three vectors. If  = 0, the weights provide positive feedback, by emphasizing the relevant documents in the initial set. If  = 0, the weights provide negative feedback, by reducing the emphasis on the non-relevant documents in the initial set.

29. 29 Adjusting Parameters 2: Filtering Incoming Messages D 1, D 2, D 3,... is a stream of incoming documents that are to be divided into two sets: R - documents judged relevant to an information need S - documents judged not relevant to the information need A query is defined as the vector in the term vector space: q = (w 1, w 2,..., w n ) where w i is the weight given to term i D j will be assigned to R if similarity(q, D j ) > What is the optimal query, i.e., the optimal values of the w i ?

30. 30 Seeking Optimal Parameters Theoretical approach Develop a theoretical model Derive parameters Test with users Heuristic approach Develop a heuristic Vary parameters Test with users Machine learning approach

31. 31 Seeking Optimal Parameters using Machine Learning GENERAL:EXAMPLE: Text RetrievalInput: training examples queries with relevance judgments design space parameters of retrieval functionTraining: automatically find the solution find parameters so that many in design space that works well relevant documents are ranked on the training data highlyPrediction: predict well on new examples rank relevant documents high also for new queries Joachims

32. 32 Task Application Text Routing Help-Desk Support: Who is an appropriate expert for a particular problem? Information Information Agents: FilteringWhich news articles are interesting to a particular person? Relevance Information Retrieval: FeedbackWhat are other documents relevant for a particular query? Text Knowledge Management: Categorization Organizing a document database by semantic categories. Machine Learning: Tasks and Applications

33. 33 Learning to Rank Assume: distribution of queries P(q) distribution of target rankings for query P(r | q) Given: collection D of documents independent, identically distributed training sample (q i, r i ) Design: set of ranking functions F loss function l(r a, r b ) learning algorithm Goal: find f  F that minimizes l(f (q), r) integrated across all queries

34. 34 A Loss Function for Rankings For two orderings r a and r b, a pair is: concordant, if r a and r b agree in their ordering P = number of concordant pairs discordant, if r a and r b disagree in their ordering Q = number of discordant pairs Loss function: l(r a, r b ) = Q Example: r a = (a, c, d, b, e, f, g, h) r b = (a, b, c, d, e, f, g, h) The discordant pairs are: (c, b), (d, b) l(r a, r b ) = 2 Joachims

35. 35 Machine Learning: Algorithms The choice of algorithms is a subject of active research, which is covered in several courses, notably CS 478 and CS/INFO 630. Some effective methods include: Naive Bayes Rocchio Algorithm C4.5 Decision Tree k-Nearest Neighbors Support Vector Machine

36. 36 Relevance Feedback: Clickthrough Data Relevance feedback methods have suffered from the unwillingness of users to provide feedback. Joachims and others have developed methods that use Clickthrough data from online searches. Concept: Suppose that a query delivers a set of hits to a user. If a user skips a link a and clicks on a link b ranked lower, then the user preference reflects rank(b) < rank(a).

37. 37 Clickthrough Example Ranking Presented to User: 1. Kernel Machines http://svm.first.gmd.de/ 2. Support Vector Machine http://jbolivar.freeservers.com/ 3. SVM-Light Support Vector Machine http://ais.gmd.de/~thorsten/svm light/ 4. An Introduction to Support Vector Machines http://www.support-vector.net/ 5. Support Vector Machine and Kernel... References http://svm.research.bell-labs.com/SVMrefs.html Ranking: (3 < 2) and (4 < 2) User clicks on 1, 3 and 4 Joachims