A language modeling framework for expert finding

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Saúl Vargas Sandoval

Educação Tecnologia

The expert finding task ,[object Object]

Document-candidate association ,[object Object],[object Object]

The expert finding: what and why? (I) ,[object Object]

161 countries ,[object Object],[object Object]

The expert finding: what and why? (II) ,[object Object]

Language modeling: the basics ,[object Object]

Query q -> probability of the document model generating the query q:

The expert finding task: formalization ,[object Object]

Bayes' Theorem and simplifying assumption:

The expert finding task: models Candidate model Document model Documents Candidates Query Result Query Ranked documents Candidates Result

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A language modeling framework for expert finding

1. A language model framework for expert finding Written by Krisztian Balog, Leif Azzopardi, Maarten de Rijke Presented by Saúl Vargas Sandoval

3. Language modeling: the basics

5. Candidate model

6. Document model

9. Questions

10.

11.

12.

13.

14. E-mail

15. database records

16. Agendas

17. Memos

18. Logs

19. Blogs

20. Address books

21. ...

22.

23.

24. Query q -> probability of the document model generating the query q:

25.

26. Bayes' Theorem and simplifying assumption:

27. The expert finding task: models Candidate model Document model Documents Candidates Query Result Query Ranked documents Candidates Result

28.

29. Smoothing:

30.

31. Let's do the math!

32.

33. First estimation:

34.

35. Second estimation:

36.

37. Association between the documents and the candidate.

38. Assuming independence:

39.

40. First estimation:

41.

42. Second estimation:

43.

44. Boolean model:

45. Frenquency-based approach: TF.IDF

46.

47. For each model: independece assumption or windows? Which window size?

48. Document-candidate association: boolean model or frequency-based approach?

49.

50.

51. Each person described in documents with name, e-mail, ID number, abreviations...

52.

53. Mean reciprocal rank (MRR):

54. Candidate model vs. Document model Model MAP MRR 2005 2006 2005 2006 Candidate 1st 0.1883 0.3206 0.4692 0.7264 Document 1st 0.2503 0.4660 0.6088 0.9354

55. Window sizes Model MAP MRR 2005 2006 2005 2006 Candidate 2nd 25 100 15 15 Document 2nd 125 250 15 75

56. Conditional independence vs. Windows (I) Model MAP MRR 2005 2006 2005 2006 Candidate 1st 0.1883 0.3206 0.4692 0.7264 Candidate 2nd 0.2020 0.4254 0.5928 0.9048 Document 1st 0.2053 0.4660 0.6088 0.9354 Document 2nd 0.2194 0.4544 0.6096 0.9235 Window sizes optimized for MAP

57. Conditional independence vs. Windows (II) Model MAP MRR 2005 2006 2005 2006 Candidate 1st 0.1883 0.3206 0.4692 0.7264 Candidate 2nd 0.2012 0.3848 0.6275 0.9558 Document 1st 0.2053 0.4660 0.6088 0.9354 Document 2nd 0.1964 0.4463 0.6371 0.9531 Window sizes optimized for MRR

58. Conditional independence vs. Windows (III)

59. Document-candidate association methods (I)

60. Conclusions DOCUMENT MODEL (with conditional independence!)

61.

62. Thanks! Danke!

A language modeling framework for expert finding

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