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Evaluating topic models for information retrieval

Authors:

Xing Yi,

James AllanAuthors Info & Claims

CIKM '08: Proceedings of the 17th ACM conference on Information and knowledge management

Pages 1431 - 1432

https://doi.org/10.1145/1458082.1458317

Published: 26 October 2008 Publication History

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Abstract

We explore the utility of different types of topic models, both probabilistic and not, for retrieval purposes. We show that: (1) topic models are effective for document smoothing; (2) more elaborate topic models that capture topic dependencies provide no additional gains; (3) smoothing documents by using their similar documents is as effective as smoothing them by using topic models; (4) topics discovered on the whole corpus are too coarse-grained to be useful for query expansion. Experiments to measure topic models' ability to predict held-out likelihood confirm past results on small corpora, but suggest that simple approaches to topic model are better for large corpora.

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Akhmedova AAmat-Lefort NBarravecchia FMastrogiacomo L(2024)WHAT CAN WE LEARN FROM THE ‘WISDOM OF CROWDS’? DRIVERS OF (DIS)SATISFACTION IN SHARED MOBILITY PLATFORMS: A COMPARISON OF FREE-FLOATING AND STATION-BASED MODELS.Journal of Cleaner Production10.1016/j.jclepro.2024.144449(144449)Online publication date: Dec-2024
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Barravecchia FMastrogiacomo LCasadesús Fa MFranceschini F(2023)MOBI-Qual: a common framework to manage the product-service system quality of shared mobilityFlexible Services and Manufacturing Journal10.1007/s10696-023-09520-y36:4(1359-1398)Online publication date: 11-Nov-2023
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Index Terms

Evaluating topic models for information retrieval
1. Information systems
  1. Information retrieval

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Published In

CIKM '08: Proceedings of the 17th ACM conference on Information and knowledge management

October 2008

1562 pages

ISBN:9781595939913

DOI:10.1145/1458082

General Chair:
James G. Shanahan
Church and Duncan Group Inc, USA
,
Program Chairs:
Sihem Amer-Yahia
Yahoo! Research, USA
,
Ioana Manolescu
INRIA, France
,
Yi Zhang
University of California, Santa Cruz, USA
,
David A. Evans
JustSystems Evans Research, USA
,
Alek Kolcz
Microsoft Live Labs, USA
,
Key-Sun Choi
KAIST, Korea
,
Abdur Chowdury
Twitter, USA

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Published: 26 October 2008

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https://doi.org/10.3390/biomedinformatics4010033
Akhmedova AAmat-Lefort NBarravecchia FMastrogiacomo L(2024)WHAT CAN WE LEARN FROM THE ‘WISDOM OF CROWDS’? DRIVERS OF (DIS)SATISFACTION IN SHARED MOBILITY PLATFORMS: A COMPARISON OF FREE-FLOATING AND STATION-BASED MODELS.Journal of Cleaner Production10.1016/j.jclepro.2024.144449(144449)Online publication date: Dec-2024
https://doi.org/10.1016/j.jclepro.2024.144449
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A Comparative Study of Utilizing Topic Models for Information Retrieval

Topic-driven reader comments summarization

Topic sentiment change analysis

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