research-article

Event-Driven Query Expansion

Authors:

Kira RadinskyAuthors Info & Claims

WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining

March 2021

Pages 391 - 399

https://doi.org/10.1145/3437963.3441833

Published: 08 March 2021 Publication History

Abstract

A significant number of event-related queries are issued in Web search. In this paper, we seek to improve retrieval performance by leveraging events and specifically target the classic task of query expansion. We propose a method to expand an event-related query by first detecting the events related to it. Then, we derive the candidates for expansion as terms semantically related to both the query and the events. To identify the candidates, we utilize a novel mechanism to simultaneously embed words and events in the same vector space. We show that our proposed method of leveraging events improves query expansion performance significantly compared with state-of-the-art methods on various newswire TREC datasets.

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Cited By

Abdollahi SKuculo TGottschalk S(2024)Event-Specific Document Ranking Through Multi-stage Query Expansion Using an Event Knowledge GraphAdvances in Information Retrieval10.1007/978-3-031-56060-6_22(333-348)Online publication date: 16-Mar-2024
https://doi.org/10.1007/978-3-031-56060-6_22
Yang WPan Y(2023)Enhancing information retrieval with semantic query expansion: a Word2Vec-based approachFourth International Conference on Signal Processing and Computer Science (SPCS 2023)10.1117/12.3012203(57)Online publication date: 21-Dec-2023
https://doi.org/10.1117/12.3012203
Haouari FElsayed TMansour W(2023)Who can verify this? Finding authorities for rumor verification in TwitterInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10336660:4Online publication date: 26-Jul-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103366
Show More Cited By

Index Terms

Event-Driven Query Expansion
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Lexical semantics
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing
      1. Query reformulation
  2. Information systems applications
    1. Digital libraries and archives

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cover image ACM Conferences

WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining

March 2021

1192 pages

ISBN:9781450382977

DOI:10.1145/3437963

General Chairs:
Liane Lewin-Eytan
Amazon, Israel
,
David Carmel
Amazon, Israel
,
Elad Yom-Tov
Microsoft, Israel
,
Program Chairs:
Eugene Agichtein
Emory University and Amazon, USA
,
Evgeniy Gabrilovich
Google Health, USA

Copyright © 2021 ACM.

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Published: 08 March 2021

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WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining

March 8 - 12, 2021

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Cited By

Abdollahi SKuculo TGottschalk S(2024)Event-Specific Document Ranking Through Multi-stage Query Expansion Using an Event Knowledge GraphAdvances in Information Retrieval10.1007/978-3-031-56060-6_22(333-348)Online publication date: 16-Mar-2024
https://doi.org/10.1007/978-3-031-56060-6_22
Yang WPan Y(2023)Enhancing information retrieval with semantic query expansion: a Word2Vec-based approachFourth International Conference on Signal Processing and Computer Science (SPCS 2023)10.1117/12.3012203(57)Online publication date: 21-Dec-2023
https://doi.org/10.1117/12.3012203
Haouari FElsayed TMansour W(2023)Who can verify this? Finding authorities for rumor verification in TwitterInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10336660:4Online publication date: 26-Jul-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103366
Vishwakarma DKumar S(2023)Analysis of Recent Query Expansion Techniques for Information Retrieval SystemsProceedings of the International Conference on Intelligent Computing, Communication and Information Security10.1007/978-981-99-1373-2_29(375-383)Online publication date: 4-Jul-2023
https://doi.org/10.1007/978-981-99-1373-2_29
Zhang YGuo FShen JHan JZhang ARangwala H(2022)Unsupervised Key Event Detection from Massive Text CorporaProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539395(2535-2544)Online publication date: 14-Aug-2022
https://doi.org/10.1145/3534678.3539395
Kalifa DSinger UGuy IRosin GRadinsky KSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Leveraging World Events to Predict E-Commerce Consumer Demand under AnomalyProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498452(430-438)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498452

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