mlsanityData to 14 Oct 2026

Entity Labels Are Not Entity Signals: A Framework for Observable Relevance in Document Re-Ranking

arXiv 2606.15998 ↗First posted 14 Jun 2026cs.IR cs.AI cs.CLcited by 1

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Abstract (arXiv)

Entity-aware document retrieval uses query-associated entities as ranking signals, assuming that semantically relevant entities are also useful retrieval signals. We show this assumption is insufficient- and explain why. Unlike terms, which are ground-truth observations, entity links are hypotheses produced by an imperfect linker: an entity can be topically central yet provide no discriminative signal if the linker fires indiscriminately across relevant and non-relevant documents. We formalize this as a distinction between Conceptual Entity Relevance (CER)- whether an entity is topically related to a query- and Observable Entity Relevance (OER)- whether its observed presence in a collection discriminates relevant from non-relevant documents. Across four collections and annotation sources including human entity judgments, CER and OER exhibit near-chance agreement (kappa ≈ 0), while OER operationalizations agree substantially (kappa ≈ 0.5), confirming CER as the systematic outlier. CER-based supervision selects topically plausible but weakly discriminative entities, pruning fewer than 4% of non-relevant documents on some collections. Aligning supervision with OER improves non-relevant pruning by up to 10x and open-world MAP by 0.051 over BM25. Our findings motivate a shift from conceptual to observable notions of entity relevance in entity-aware retrieval.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 34 references that are in the map. First posted on arXiv 14 Jun 2026.

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17 of 34 references.

References · 17

  1. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  2. 2020Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrievalcited by 849
  3. 2020Overview of the TREC 2019 deep learning trackcited by 286
  4. 2023RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!cited by 155
  5. 2022RankT5: Fine-Tuning T5 for Text Ranking with Ranking Lossescited by 115
  6. 2017Neural Ranking Models with Weak Supervisioncited by 61
  7. 2018Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrievalcited by 33
  8. 2016Query Expansion with Locally-Trained Word Embeddingscited by 17
  9. 2017Word-Entity Duet Representations for Document Rankingcited by 16
  10. 2016Using Word Embeddings for Automatic Query Expansioncited by 14
  11. 2020Graph-Embedding Empowered Entity Retrievalcited by 10
  12. 2024DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entitiescited by 8
5 more
  1. 2018Deep Neural Networks for Query Expansion using Word Embeddingscited by 7
  2. 2022CODEC: Complex Document and Entity Collectioncited by 7
  3. 2024DREQ: Document Re-Ranking Using Entity-based Query Understandingcited by 5
  4. 2025QDER: Query-Specific Document and Entity Representations for Multi-Vector Document Re-Rankingcited by 1
  5. 2026Entities as Retrieval Signals: A Systematic Study of Coverage, Supervision, and Evaluation in Entity-Oriented Rankingcited by 1

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