A language modeling framework for expert finding

•

0 gostou•492 visualizações

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

Mais conteúdo relacionado

Destaque

Cisco at v mworld 2015 theater presentation brfarnhaldangelo0772

検証環境をGoBGPで極力仮想化してみたToshiya Mabuchi

AgreeablenessFrank Calberg

CSS/Financial Aid PROFILELisa Allard

비콘커뮤니케이션 Credential 2015_ver1.0영현 김

Resume Tips 2016 Do's and Don'tsLatest Resume Tips

MP BGP-EVPN 실전기술-1편(개념잡기)JuHwan Lee

Presentación Hernan Litvac - eCommerce Day Santiago 2016eCommerce Institute

REVISÃO ESTRATÉGIA PARA OAB DIPEsdras Arthur Lopes Pessoa

WDM principlesAnuradha Udunuwara

Destaque (10)

Cisco at v mworld 2015 theater presentation brfarnha

検証環境をGoBGPで極力仮想化してみた

Agreeableness

CSS/Financial Aid PROFILE

비콘커뮤니케이션 Credential 2015_ver1.0

Resume Tips 2016 Do's and Don'ts

MP BGP-EVPN 실전기술-1편(개념잡기)

Presentación Hernan Litvac - eCommerce Day Santiago 2016

REVISÃO ESTRATÉGIA PARA OAB DIP

WDM principles

Semelhante a A language modeling framework for expert finding

Metadata Quality Assurance Part II. The implementation beginsPéter Király

CSE NEW_4th yr w.e.f. 2018-19.pdfssuser5a7261

Human-Centered Interpretable Machine LearningPrzemek Biecek

Open elective iii & iv list 2021 22pcjoshi02

Knowledge graphs for knowing more and knowing for sureSteffen Staab

3 rd International Conference on Data Science and Machine Learning (DSML 2022)ijscai

3rd International Conference on Cloud, Big Data and Web Services (CBW 2022)ijccsa

3 rd International Conference on Cloud, Big Data and Web Services (CBW 2022)ijait

4th International Conference on Data Mining and NLP (DNLP 2023) mlaij

3 rd International Conference on Data Science and Machine Learning (DSML 2022)gerogepatton

2nd International Conference on Data Mining, Big Data and Machine Learning (D...IJITCA Journal

Data Science & Big Data - Theory.pdfRAKESHG79

RES812 U4 Individual ProjectThienSi Le

Conference Identity: persistent identifiers for conferencesAliaksandr Birukou

Measuring Metadata Quality (ELAG, 2018)Péter Király

Data Mining Xuequn Shang NorthWestern Polytechnical Universitybutest

Probabilistic Topic modelsCarlos Badenes-Olmedo

Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian

Semelhante a A language modeling framework for expert finding (20)

Metadata Quality Assurance Part II. The implementation begins

CSE NEW_4th yr w.e.f. 2018-19.pdf

Human-Centered Interpretable Machine Learning

Open elective iii & iv list 2021 22

Knowledge graphs for knowing more and knowing for sure

3 rd International Conference on Data Science and Machine Learning (DSML 2022)

3rd International Conference on Cloud, Big Data and Web Services (CBW 2022)

3 rd International Conference on Cloud, Big Data and Web Services (CBW 2022)

4th International Conference on Data Mining and NLP (DNLP 2023)

3 rd International Conference on Data Science and Machine Learning (DSML 2022)

2nd International Conference on Data Mining, Big Data and Machine Learning (D...

Data Science & Big Data - Theory.pdf

RES812 U4 Individual Project

Conference Identity: persistent identifiers for conferences

Measuring Metadata Quality (ELAG, 2018)

Data Mining Xuequn Shang NorthWestern Polytechnical University

Probabilistic Topic models

Data Science as a Service: Intersection of Cloud Computing and Data Science

Último

APM Welcome, APM North West Network Conference, Synergies Across SectorsAssociation for Project Management

Q4-W6-Restating Informational Text Grade 3JemimahLaneBuaron

Beyond the EU: DORA and NIS 2 Directive's Global ImpactPECB

Software Engineering Methodologies (overview)eniolaolutunde

Sanyam Choudhary Chemistry practical.pdfsanyamsingh5019

A Critique of the Proposed National Education Policy ReformChameera Dedduwage

Measures of Central Tendency: Mean, Median and ModeThiyagu K

Nutritional Needs Presentation - HLTH 104misteraugie

BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdfSoniaTolstoy

Grant Readiness 101 TechSoup and Remy ConsultingTechSoup

The Most Excellent Way | 1 Corinthians 13Steve Thomason

SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptxiammrhaywood

microwave assisted reaction. General introductionMaksud Ahmed

social pharmacy d-pharm 1st year by Pragati K. Mahajanpragatimahajan3

Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Krashi Coaching

Código Creativo y Arte de Software | Unidad 1Maestría en Comunicación Digital Interactiva - UNR

Advanced Views - Calendar View in Odoo 17Celine George

IGNOU MSCCFT and PGDCFT Exam Question Pattern: MCFT003 Counselling and Family...PsychoTech Services

Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...EduSkills OECD

Class 11th Physics NEET formula sheet pdfAyushMahapatra5

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

Recomendados

Recomendados

Mais conteúdo relacionado

Destaque

Destaque (10)

Semelhante a A language modeling framework for expert finding

Semelhante a A language modeling framework for expert finding (20)

Último

Último (20)

A language modeling framework for expert finding