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DREQ: Document Re-ranking Using Entity-Based Query Understanding

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Advances in Information Retrieval (ECIR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14608))

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Abstract

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 large-scale 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.

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Notes

  1. 1.

    Code and data: https://github.com/shubham526/ECIR2024-DREQ.

  2. 2.

    Term coined by Dr. Laura Dietz at the SIGIR 2023 tutorial (https://github.com/laura-dietz/neurosymbolic-representations-for-IR).

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  1. University of Edinburgh, Edinburgh, UK

    Shubham Chatterjee & Jeff Dalton

  2. University of Glasgow, Glasgow, UK

    Iain Mackie

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  1. Shubham Chatterjee
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  1. Georgetown University, Washington, WA, USA

    Nazli Goharian

  2. University of Pisa, Pisa, Italy

    Nicola Tonellotto

  3. King's College London, London, UK

    Yulan He

  4. University College London, London, UK

    Aldo Lipani

  5. University of Glasgow, Glasgow, UK

    Graham McDonald

  6. University of Glasgow, Glasgow, UK

    Craig Macdonald

  7. University of Glasgow, Glasgow, UK

    Iadh Ounis

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Chatterjee, S., Mackie, I., Dalton, J. (2024). DREQ: Document Re-ranking Using Entity-Based Query Understanding. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14608. Springer, Cham. https://doi.org/10.1007/978-3-031-56027-9_13

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