Chapter 5 Query Operations

1. Chapter 5Query Operations Hsin-Hsi Chen Department of Computer Science and Information Engineering National Taiwan University

2. Paraphrase Problem in IR • Users often input queries containing terms that do not match the terms used to index the majority of the relevant documents. • relevance feedback and query modification • reweighting of the query terms based on the distribution of these terms in the relevant and nonrelevant documents retrieved in response to those queries • changing the actual terms in the query

3. Query Reformulation • basic steps • query expansion: expanding the original query with new terms • feedback information from the user • information derived from the set of documents initially retrieved (local set of documents) • global information derived from document collection • term reweighting • reweighting the terms in the expanded query

4. User Relevance Feedback • U: Query is submitted • S: A list of the retrieved documents is presented • U: The documents are examined and the relevant ones are marked • S: The important terms/expressions are selected from the documents that have been identified as relevant • The relevance feedback cycle is repeated several times

5. User Relevance Feedback (Continued) • advantages • Shield the details of the query reformulation • Break down the whole searching task into a sequence of small steps • Provide a controlled process designed to emphasize some terms (relevant ones) and de-emphasize others (non-relevant ones)

6. Query Expansion and Term Reweighting for the Vector Model • basic idea • Relevant documents resemble each other • Non-relevant documents have term-weight vectors which are dissimilar from the ones for the relevant documents • The reformulated query is moved to closer to the term-weight vector space of relevant documents

8. Query Expansion and Term Reweighting for the Vector Model (Continued) Dr: set of relevant documents, as identified by the user Dn: set of non-relevant documents the retrieved documents collection Cr: set of relevant documents set of non-relevant documents

9. Query Expansion and Term Reweighting for the Vector Model (Continued) • when complete set Cr of relevant documents is known • when the set Cr are not known a priori • Formulate an initial query • Incrementally change the initial query vector

10. query expansion • Calculate the modified query • Standard-Rochio • Ide-Regular • Ide-Dec-Hi • , , : tuning constants (usually, >) • =1 (Rochio, 1971) • ===1 (Ide, 1971) • =0: positive feedback term reweighting the highest ranked non-relevant document Similar performance

11. positive relevance-feedback ==1 and =0

12. “dec hi” method: use all relevant information, but subtract only the highest ranked nonrelevant document • feedback with query splittingsolve problems: (1) the relevant documents identified do not form a tight cluster; (2) nonrelevant documents are scattered among certain relevant ones homogeneous relevant items homogeneous relevant items

13. Analysis • advantages • simplicity • good results • disadvantages • No optimality criterion is adopted

14. Term Weighting for the Probabilistic Model • The similarity of a document dj to a query q : the probability of observing the term ki in the set R of relevant documents : the probability of observing the term ki in the set R of non-relevant documents Initial search:

15. Initial search: Feedback search:

16. Feedback search: No query expansion occurs

17. For small values of |Dr| and |Dr,i| (i.e., |Dr|=1, |Dr,i|=0) Alternative 1: Alternative 2:

18. Analysis • advantages • Feedback process is directly related to the derivation of new weights for query terms • The term reweighting is optimal • disadvantages • Document term weights are not considered • Weights of terms in previous query formulations are disregarded • No query expansion is used

19. A Variant of Probabilistic Term Reweighting • variant • distinct initial search method • include within-document frequency weights • initial search Similar to tf-idf scheme

20. C=0 for automatically indexed collections or for feedback searching (allow IDF or the relevance weighting to be the dominant factor) C>0 for manually indexed collections (allow the mere existence of a term within a document to carry more weight) K=0.3 for initial search of regular length documents (documents having many multiple occurrences of a term) K=0.5 for feedback searches K=1 for short documents: the within-document frequency is removed (the within-document frequency plays a minimum role) Feedback search

21. Analysis • advantages • The within-document frequencies are considered • A normalized version of these frequencies is adopted • Constants C and K are introduced • disadvantages • more complex formulation • no query expansion

22. Evaluation of relevance feedback • Standard evaluation (i.e., recall-precision) method is not suitable, because the relevant documents used to reweight the query terms moving to higher ranks. • The residual collection method • the evaluation of the results compares only the residual collections, i.e., the initial run is remade minus the documents previously shown to the user and this is compared with the feedback run minus the same documents Note that qm tend to be lower than the figures for the original query vector q in residual collection

23. Residual Collection with Partial Rank Freezing • The previously retrieved items identified as relevant are kept “frozen”; and the previously retrieved nonrelevant items are simple removed from the collection. Assume 10 documents are relevant.

24. Residual Collection with Partial Rank Freezing

25. Automatic Local Analysis • user relevance feedback • Known relevant documents contain terms which can be used to describe a larger cluster of relevant documents with assistance from the user (clustering) • automatic analysis • Obtain a description (i.t.o terms) for a larger cluster of relevant documents automatically • global strategy: global thesaurus-like structure is trained from all documentsbefore querying • local strategy: terms from the documents retrieved for a given query are selected at query time

26. Local Feedback Strategy • Internet • client site • Retrieving the text of 100 Web documents for local analysis would take too long • server site • Analyzing the text of 100 Web documents would spend extra CPU time • Applications • Intranet • Specialized document collections, e.g., medical document collections

27. Query Expansion-Local Clustering • stem • V(s): a non-empty subset of words which are grammatical variants of each othere.g., {polish, polishing, polished} • A canonical form s of V(s) is called a steme.g., polish • local document set Dl • the set of documents retrieved for a given query • local vocabulary Vl (Sl) • the set of all distinct words (stems) in the local document set

28. local cluster • basic concept • Expanding the query with terms correlated to the query terms • The correlated terms are presented in the local clusters built from the local document set • local clusters • association clusters: co-occurrences of pairs of terms in documents • metric clusters: distance factor between two terms • scalar clusters: terms with similar neighborhoods have some synonymity relationship

29. Association Clusters • idea • Based on the co-occurrence of stems (or terms) inside documents • association matrix • fsi,j: the frequency of a stem si in a document dj (Dl) • m=(fsi,j): an association matrix with |Sl| rows and |Dl| columns • : a local stem-stem association matrix

30. : a correlation between the stems su and sv an element in su,v=cu,v: unnormalized matrix : normalized matrix local association cluster around the stem su Take u-th row Return the set of n largest values su,v (uv)

31. Metric Clusters • idea • Consider the distance between two terms in the computation of their correlation factor • local stem-stem metric correlation matrix • r(ki,kj): the number of words between keywords ki and kj in a same document • cu,v: metric correlation between stems su and sv

32. su,v=cu,v: unnormalized matrix : normalized matrix local metric cluster around the stem su Take u-th row Return the set of n largest values su,v (uv)

33. Scalar Clusters The row corresponding to a specific term in a term co-occurrence matrix forms its neighborhood • idea • Two stems with similar neighborhoods have synonymity relationship • The relationship is indirect or induced by the neighborhood • scalar association matrix The correlation value for su and sv in this matrix may be small local scalar cluster around the stem su Take u-th row Return the set of n largest values su,v (uv)

34. x x x x x x Sv(n) x x Su x x x x Sv x x x x x x x x Interactive Search Formulation • neighbors of the query term sv • Terms su belonging to clusters associated to sv, i.e., suSv(n) • su is called a searchonym of sv

35. Interactive Search Formulation(Continued) • Algorithm • For each stem svq, select m neighbor stems from the cluster Sv(n) and add them to the query • Merge normalized and unnormalized clusters • Extension • Let su and sv be correlated with a cu,v • If cu,v is larger than a predefined threshold, then a neighbor stem su’ of su can also be interpreted as a neighbor stem of sv, and vice versa. more rare large frequencies

36. Query Expansion throughLocal Context Analysis • local analysis • Based on the set of documents retrieved for the original query • Based on term co-occurrence inside documents • Terms closest to individual query terms are selected • global analysis • Based on the whole document collection • Based on term co-occurrence inside small contexts and phrase structures • Terms closest to the whole query are selected

37. Query Expansion throughLocal Context Analysis (Continued) • candidates • noun groups instead of simple keywords • single noun, two adjacent nouns, or three adjacent nouns • query expansion • Concepts are selected from the top ranked documents (as in local analysis) • Passages are used for determining co-occurrence (as in global analysis)

38. Query Expansion throughLocal Context Analysis (Continued) • algorithm • Retrieve the top n ranked passages using the original query • For each concept in the top ranked passages, the similarity sim(q,c) between the whole query q and the concept c is computed using a variant of tf-idf ranking • The top m ranked concepts are added to the original query q • Each concept is assigned a weight 1-0.9i/m (i: rank) • Each term in the original query is assigned a weight 2original weight

39. n: # of ranked passages for infrequent query term 0.1 correlation between c and ki pfi,j (pfc,j): freq of ki (c) in j-th passage association clusters (passage) N: # of passages in the collection npi: # of passages containing term ki npc: # of passages containing concept c idf1，當np很大(小)時，第二項可能小(大)於1

40. Automatic Global Analysis • local analysis • Extract information from the local set of documents (passages) retrieved • global analysis • Expand the query using information from the whole set of documents in the collection • Issues • How to build the thesaurus • How to select the terms for query expansion • Query expansion based on similarity thesaurus • Query expansion based on statistical thesaurus

41. Similarity Thesaurus • How to build the thesaurus • Consider term to term relationship instead of co-occurrence • How to select the terms for query expansion • Consider the similarity to the whole query instead of individual query terms

42. Concept Space • basic idea • Each term is indexed by the documents in which it appears • The role of terms and documents is interchanged in the concept space • t: the number of terms in the collection • N: the number of documents in the collection • fi,j: the frequency of term ki in document dj • tj: the number of distinct index terms in document dj • itfj: inverse term frequency for document dj (dj 用來區辨index term的能力， dj含有的index terms 越多，區辨力 越低)

43. Each term ki is associated with a vector ki where The relationship between two terms ku and kv is computed as

44. Query Expansion using Global Similarity Thesaurus • Represent the query in the concept space used for representation of the index terms • Based on the global similarity thesaurus, compute a similarity sim(q,kv) between each term kv correlated to the query terms and the whole query q query term expand term

45. Query Expansion using Global Similarity Thesaurus • Expand the query with the top r ranked terms according to sim(q,kv)

46. Kv Kj Ka Kb Qc Q={Ka,Kb} Ki Expand term

47. GVSM vs. Query Expansion Only the top r ranked terms are used for query expansion.