DREQ: Document Re-Ranking Using Entity-based Query Understanding
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909069785169920 |
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| author | Chatterjee, Shubham Mackie, Iain Dalton, Jeff |
| author_facet | Chatterjee, Shubham Mackie, Iain Dalton, Jeff |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05939 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DREQ: Document Re-Ranking Using Entity-based Query Understanding Chatterjee, Shubham Mackie, Iain Dalton, Jeff Information Retrieval Artificial Intelligence 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. |
| title | DREQ: Document Re-Ranking Using Entity-based Query Understanding |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2401.05939 |