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İ rem Ar ıkan , Srikanta Bedathur, Klaus Berberich

Time Will Tell: Leveraging Temporal Expressions in IR. İ rem Ar ıkan , Srikanta Bedathur, Klaus Berberich. Motivation. Documents contain temporal information in the form of temporal expressions. Motivation. Documents contain temporal information in the form of temporal expressions.

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İ rem Ar ıkan , Srikanta Bedathur, Klaus Berberich

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  1. Time Will Tell: Leveraging Temporal Expressions in IR İrem Arıkan, Srikanta Bedathur, Klaus Berberich

  2. Motivation • Documents contain temporal information in the form of temporal expressions

  3. Motivation • Documents contain temporal information in the form of temporal expressions

  4. Motivation • Users have temporal information needs • Query: Prime Minister United Kingdom2000

  5. Motivation • Users have temporal information needs • Query: Prime Minister United Kingdom2000 PROBLEM Traditional information retrieval systems do not exploit the temporal content in documents Temporal expressions are more than common terms

  6. Motivation • Users have temporal information needs • Query: Prime Minister United Kingdom2000 PROBLEM Traditional information retrieval systems do not exploit the temporal content in documents Temporal expressions are more than common terms OUR APPROACH Integratestemporal dimensioninto a language model basedretrievalframework

  7. Outline Motivation Model Our Approach Experimental Evaluation

  8. Document Model • Documentd = { dtext,dtemp} • dtext: a bag of textual terms • dtemp: a bag of temporal expressions

  9. Document Model • Documentd = { dtext,dtemp} • dtext: a bag of textual terms • dtemp: a bag of temporal expressions • a temporal expression is considered as a time interval T = [begin,end ] T [ ] 0 begin end

  10. Query Model • Query q= { qtext,qtemp} • qtext: set of textual terms • qtemp: set of temporal expressions • Prime Minister United Kingdom 2000 qtext qtemp

  11. Outline • Motivation • Model • Our Approach • Filtering Approach • Weighted Approach • Experimental Evaluation

  12. Our Baseline: Ponte and Croft‘s Model (LM) • Each document has a language model associated • Query is a random process • Documents are ranked according to the likelihood that the query would be generated by the language model estimated for each document

  13. Filtering Approach (LMF) • Idea: Discard all documents that do not contain any temporal expression relevant to the user‘s query t

  14. Filtering Approach • Idea: Discard all documents that do not contain any temporal expression relevant to the user‘s query • our definition of temporal relevance • only relevant, if it overlaps with a temporal expression from the query 2 May 1997 – 27 June 2007 28 Nov 1990 - 2 May 1997 2000 query t begin end

  15. Filtering Approach • Idea: Discard all documents that do not contain any relevant temporal expressions to user‘s query • our definition of temporal relevance • only relevant, if it overlaps with a temporal expression from the query • Relevant 2 May 1997 – 27 June 2007 X Irrelevant 28 Nov 1990 - 2 May 1997 2000 query t begin end

  16. Filtering Approach • Problem:has a black-and-white view of the world • Does not take into account • how many relevant temporal expressions a document contains • how closely they match the temporal expressions specified in the user‘s query

  17. Filtering Approach • Problem:has a black-and-white view of the world • Does not take into account • how many relevant temporal expressions a document contains • how closely they match the temporal expressions specified in the user‘s query • query: 1980 – 1990 1980 – 1989 is more relevant than 23 March 1984

  18. Weighted Approach (LMW) • Idea: Assign higher relevance to a document, if it contains more temporal expressions that match more closely to the temporal expressions from the user‘s query

  19. Weighted Approach • Idea: Assign higher relevance to a document, if it contains more temporal expressions that match more closely to the temporal expressions from the user‘s query • We assume that qtext and qtemp are produced independently

  20. Weighted Approach • Idea: Assign higher relevance to a document, if it contains more temporal expressions that match more closely to the temporal expressions from the user‘s query • We assume that qtext and qtemp are produced independently • Temporal expressions occur independently

  21. Weighted Approach • Each temporal expression T in d is a sample from a different generative model

  22. Weighted Approach • Each temporal expression T in d is a sample from a different generative model • Generating a temporal expression Q = [qBegin, qEnd] given dtemp • draw a single temporal expression T=[dBegin, dEnd] at uniform from d • generate Q by the generative model that is associated with T

  23. Weighted Approach • Each temporal expression T in d is a sample from a different generative model • Generating a temporal expression Q = [qBegin, qEnd] given dtemp • draw a single temporal expression T=[dBegin, dEnd] at uniform from d • generate Q by the generative model that is associated with T • The likelihood of generating Q by the set of generative models that produced dtemp

  24. Weighted Approach • Generate Q=[qBegin, qEnd]from the query by the generative model that is associated with T = [dBegin, dEnd] from a document P(qBegin) P(qEnd|qBegin) dBegin-α(dEnd-dBegin) qBegin dBegin dEnd qBegin qEnd dEnd dEnd+α(dEnd-dbegin)

  25. Weighted Approach • Generate Q=[qBegin, qEnd]from the query by the generative model that is associated with T = [dBegin, dEnd] from a document P(qBegin) P(qEnd|qBegin) dBegin -α(dEnd-dBegin) qBegin dBegin dEnd qBegin qEnd dEnd dEnd +α(dEnd-dbegin) produces only relevant temporal expressions of T P(Q|T) gets smaller as the length of their overlap decreases

  26. Outline Motivation Model Our Approach Experimental Evaluation

  27. Experimental Evaluation Dataset HTML snapshot of English Wikipedia from May 2007 containing ~ 2M documents Implementation • Terrier Information Retrieval Platform: • provides an implementation of Ponte & Croft's approach • LMF, LMW • Java + MySQL • A set of regular expressions for extracting temporal information

  28. Experimental Evaluation Spanish painter 18th century Anectodal query results - 1

  29. Experimental Evaluation Sea Battle 1650 - 1670 Anectodal query results - 2

  30. Experimental Evaluation User Study • 20 queries • Pooling top-10 results returned by the three methods • Relevance assessment by 15 users • highly relevant: 2 • marginally relevant: 1 • irrelevant: 0 • NDCG as a measure of effectiveness

  31. Experimental Evaluation

  32. Thank you! Questions?

  33. Conclusion • Documents are rich of temporal expressions, but existing retrieval models are ignorant of their inherent semantics • Our work proposes two methods addressing this problem • Initial experimental evidence shows that our methods improve retrieval effectiveness for temporal information needs

  34. Experimental Evaluation

  35. b’ e e+α(e-b) Weighted Approach • generative model associated with T =[b,e] P(b’) P(e’) b e b-α(e-b) only generates overlapping intervals of T P(b’,e’) ~ |overlap|

  36. Our Baseline: Ponte and Croft‘s Model (LM) • Query likelihood: the likelihood that a query q and a document d is generated by the same language model • depends on the term frequency of query words in the document and their collection frequency

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