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Topic modeling for expert finding using latent Dirichlet allocation

Authors:

Saeedeh Momtazi,

Felix NaumannAuthors Info & Claims

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Volume 3, Issue 5

Pages 346 - 353

https://doi.org/10.1002/widm.1102

Published: 01 September 2013 Publication History

Abstract

The task of expert finding is to rank the experts in the search space given a field of expertise as an input query. In this paper, we propose a topic modeling approach for this task. The proposed model uses latent Dirichlet allocation LDA to induce probabilistic topics. In the first step of our algorithm, the main topics of a document collection are extracted using LDA. The extracted topics present the connection between expert candidates and user queries. In the second step, the topics are used as a bridge to find the probability of selecting each candidate for a given query. The candidates are then ranked based on these probabilities. The experimental results on the Text REtrieval Conference TREC Enterprise track for 2005 and 2006 show that the proposed topic-based approach outperforms the state-of-the-art profile- and document-based models, which use information retrieval methods to rank experts. Moreover, we present the superiority of the proposed topic-based approach to the improved document-based expert finding systems, which consider additional information such as local context, candidate prior, and query expansion.

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Cited By

Etemadi RZihayat MFeng KAdelman JZarrinkalam FBagheri ESakai TIshita EOhshima HHasibi FMao JJose J(2024)It Takes a Team to Triumph: Collaborative Expert Finding in Community QA NetworksProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698404(164-174)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698404
Hadizadeh Moghaddam AMomtazi S(2024)A semantic modular framework for events topic modeling in social mediaMultimedia Tools and Applications10.1007/s11042-023-15745-883:4(10755-10778)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15745-8
Wang YLiu JXu XKe XWu TGou X(2023)Efficient and Effective Academic Expert Finding on Heterogeneous Graphs through (k, 𝒫)-Core based EmbeddingACM Transactions on Knowledge Discovery from Data10.1145/357836517:6(1-35)Online publication date: 22-Mar-2023
https://dl.acm.org/doi/10.1145/3578365
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cover image Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery Volume 3, Issue 5

September 2013

47 pages

EISSN:1942-4795

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John Wiley & Sons, Inc.

United States

Publication History

Published: 01 September 2013

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Cited By

Etemadi RZihayat MFeng KAdelman JZarrinkalam FBagheri ESakai TIshita EOhshima HHasibi FMao JJose J(2024)It Takes a Team to Triumph: Collaborative Expert Finding in Community QA NetworksProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698404(164-174)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698404
Hadizadeh Moghaddam AMomtazi S(2024)A semantic modular framework for events topic modeling in social mediaMultimedia Tools and Applications10.1007/s11042-023-15745-883:4(10755-10778)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15745-8
Wang YLiu JXu XKe XWu TGou X(2023)Efficient and Effective Academic Expert Finding on Heterogeneous Graphs through (k, 𝒫)-Core based EmbeddingACM Transactions on Knowledge Discovery from Data10.1145/357836517:6(1-35)Online publication date: 22-Mar-2023
https://dl.acm.org/doi/10.1145/3578365
Zhu LZhang JCunningham S(2022)Domain expertise extraction for finding rising starsScientometrics10.1007/s11192-022-04492-6127:9(5475-5495)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s11192-022-04492-6
Ghasemi NFatourechi RMomtazi S(2021)User Embedding for Expert Finding in Community Question AnsweringACM Transactions on Knowledge Discovery from Data10.1145/344130215:4(1-16)Online publication date: 26-Mar-2021
https://dl.acm.org/doi/10.1145/3441302
de Campos LFernández-Luna JHuete JRedondo-Expósito L(2021)LDA-based term profiles for expert finding in a political settingJournal of Intelligent Information Systems10.1007/s10844-021-00636-x56:3(529-559)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s10844-021-00636-x
Yuan SZhang YTang JHall WCabotà J(2020)Expert finding in community question answering: a reviewArtificial Intelligence Review10.1007/s10462-018-09680-653:2(843-874)Online publication date: 1-Feb-2020
https://dl.acm.org/doi/10.1007/s10462-018-09680-6
Dehghan MAbin A(2019)Translations Diversification for Expert FindingACM Transactions on Knowledge Discovery from Data10.1145/332048913:3(1-20)Online publication date: 29-May-2019
https://dl.acm.org/doi/10.1145/3320489
Dargahi Nobari ASotudeh Gharebagh SNeshati MKando NSakai TJoho HLi Hde Vries AWhite R(2017)Skill Translation Models in Expert FindingProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3080719(1057-1060)Online publication date: 7-Aug-2017
https://dl.acm.org/doi/10.1145/3077136.3080719
Paul SKaye JDruin ALampe CMorris DHourcade J(2016)Find an ExpertProceedings of the 2016 CHI Conference on Human Factors in Computing Systems10.1145/2858036.2858131(3038-3048)Online publication date: 7-May-2016
https://dl.acm.org/doi/10.1145/2858036.2858131
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