Details
| Original language | English |
|---|---|
| Title of host publication | Advances in Information Retrieval |
| Subtitle of host publication | 46th European Conference on Information Retrieval, ECIR 2024 |
| Editors | Nazli Goharian, Nicola Tonellotto, Yulan He, Aldo Lipani, Graham McDonald, Craig Macdonald, Iadh Ounis |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 333-348 |
| Number of pages | 16 |
| ISBN (electronic) | 978-3-031-56060-6 |
| ISBN (print) | 9783031560590 |
| Publication status | Published - 16 Mar 2024 |
| Event | 46th European Conference on Information Retrieval, ECIR 2024 - Glasgow, United Kingdom (UK) Duration: 24 Mar 2024 → 28 Mar 2024 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 14609 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Event-specific document ranking is a crucial task in supporting users when searching for texts covering events such as Brexit or the Olympics. However, the complex nature of events involving multiple aspects like temporal information, location, participants and sub-events poses challenges in effectively modelling their representations for ranking. In this paper, we propose MusQuE (Multi-stage Query Expansion), a multi-stage ranking framework that jointly learns to rank query expansion terms and documents, and in this manner flexibly identifies the optimal combination and number of expansion terms extracted from an event knowledge graph. Experimental results show that MusQuE outperforms state-of-the-art baselines on MS-MARCOEVENT, a new dataset for event-specific document ranking, by 9.1% and more.
Keywords
- Document Retrieval, Event Knowledge Graphs, Event-specific Document Ranking, Query Expansion
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
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Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024. ed. / Nazli Goharian; Nicola Tonellotto; Yulan He; Aldo Lipani; Graham McDonald; Craig Macdonald; Iadh Ounis. Springer Science and Business Media Deutschland GmbH, 2024. p. 333-348 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14609 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Event-Specific Document Ranking Through Multi-stage Query Expansion Using an Event Knowledge Graph
AU - Abdollahi, Sara
AU - Kuculo, Tin
AU - Gottschalk, Simon
N1 - Funding Information: This work was partially funded by the Federal Ministry for Economic Affairs and Climate Action (BMWK), Germany (“ATTENTION!”, 01MJ22012D).
PY - 2024/3/16
Y1 - 2024/3/16
N2 - Event-specific document ranking is a crucial task in supporting users when searching for texts covering events such as Brexit or the Olympics. However, the complex nature of events involving multiple aspects like temporal information, location, participants and sub-events poses challenges in effectively modelling their representations for ranking. In this paper, we propose MusQuE (Multi-stage Query Expansion), a multi-stage ranking framework that jointly learns to rank query expansion terms and documents, and in this manner flexibly identifies the optimal combination and number of expansion terms extracted from an event knowledge graph. Experimental results show that MusQuE outperforms state-of-the-art baselines on MS-MARCOEVENT, a new dataset for event-specific document ranking, by 9.1% and more.
AB - Event-specific document ranking is a crucial task in supporting users when searching for texts covering events such as Brexit or the Olympics. However, the complex nature of events involving multiple aspects like temporal information, location, participants and sub-events poses challenges in effectively modelling their representations for ranking. In this paper, we propose MusQuE (Multi-stage Query Expansion), a multi-stage ranking framework that jointly learns to rank query expansion terms and documents, and in this manner flexibly identifies the optimal combination and number of expansion terms extracted from an event knowledge graph. Experimental results show that MusQuE outperforms state-of-the-art baselines on MS-MARCOEVENT, a new dataset for event-specific document ranking, by 9.1% and more.
KW - Document Retrieval
KW - Event Knowledge Graphs
KW - Event-specific Document Ranking
KW - Query Expansion
UR - http://www.scopus.com/inward/record.url?scp=85189350874&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-56060-6_22
DO - 10.1007/978-3-031-56060-6_22
M3 - Conference contribution
AN - SCOPUS:85189350874
SN - 9783031560590
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 333
EP - 348
BT - Advances in Information Retrieval
A2 - Goharian, Nazli
A2 - Tonellotto, Nicola
A2 - He, Yulan
A2 - Lipani, Aldo
A2 - McDonald, Graham
A2 - Macdonald, Craig
A2 - Ounis, Iadh
PB - Springer Science and Business Media Deutschland GmbH
T2 - 46th European Conference on Information Retrieval, ECIR 2024
Y2 - 24 March 2024 through 28 March 2024
ER -