Entity Labels Are Not Entity Signals: A Framework for Observable Relevance in Document Re-Ranking
arXiv:2606.15998v1 Announce Type: cross Abstract: 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 Rel
The increasing sophistication and pervasive deployment of AI systems, particularly in information retrieval, necessitates a deeper understanding of underlying mechanisms to improve performance and reliability.
This research provides a more rigorous framework for evaluating and improving entity-aware information retrieval, which is crucial for advanced AI applications and the accuracy of automated knowledge systems.
The distinction between entity labels and entity signals will lead to more robust and accurate document re-ranking mechanisms, improving the efficacy of search and information synthesis platforms.
- · AI developers
- · Search engine companies
- · Knowledge management platforms
- · End-users of information systems
- · Less robust entity linking models
- · Information systems relying on superficial entity analysis
Improved accuracy and relevance in AI-driven information retrieval and question-answering systems.
Accelerated development of more sophisticated and 'intelligent' AI agents that rely on high-fidelity information extraction.
Enhanced ability for AI to process complex, unstructured data, potentially leading to breakthroughs in scientific discovery and decision-making.
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Read at arXiv cs.CL