DREQ: Document Re-Ranking Using Entity-based Query Understanding

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

While entity-oriented neural IR models have advanced significantly, they often overlook a key nuance: the varying degrees of influence individual entities within a document have on its overall relevance. Addressing this gap, we present DREQ, an entity-oriented dense document re-ranking model. Uniquely, we emphasize the query-relevant entities within a document’s representation while simultaneously attenuating the less relevant ones, thus obtaining a query-specific entity-centric document representation. We then combine this entity-centric document representation with the text-centric representation of the document to obtain a “hybrid” representation of the document. We learn a relevance score for the document using this hybrid representation. Using four largescale benchmarks, we show that DREQ outperforms state-of-the-art neural and non-neural re-ranking methods, highlighting the effectiveness of our entity-oriented representation approach.
Original languageEnglish
Title of host publicationProceedings of the 46th European Conference on Information Retrieval
Number of pages20
ISBN (Electronic)978-3-031-56027-9
ISBN (Print)978-3-031-56026-2
Publication statusPublished - 20 Mar 2024
Event46th European Conference on Information Retrieval - Glasgow, United Kingdom
Duration: 24 Mar 202428 Mar 2024
Conference number: 46

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference46th European Conference on Information Retrieval
Abbreviated titleECIR 2024
Country/TerritoryUnited Kingdom
Internet address


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