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Research Explains Query Expansion Performance in Information Retrieval

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the variability in query expansion (QE) performance within information retrieval (IR) systems. The study proposes two complementary perspectives to explain these differences: the concept of an Ideal Expanded Query (IEQ) and a separability measure based on Cohen's d. Experiments conducted on several TREC collections indicate that expanded queries closer to the IEQ generally lead to higher retrieval effectiveness, and the separability of relevant and non-relevant documents offers an additional insight into QE performance. AI

RANK_REASON The cluster contains a research paper published on arXiv detailing a new methodology for understanding query expansion performance in information retrieval. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Research Explains Query Expansion Performance in Information Retrieval

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The cluster contains a research paper published on arXiv detailing a new methodology for understanding query expansion performance in information retrieval. [lever_c_demoted from research: ic=1 ai=…
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Mandar Mitra ·

    Towards Explaining Query Expansion Performance in Information Retrieval

    Query Expansion (QE) techniques have long been widely used in Information Retrieval (IR) to address the vocabulary mismatch problem. They remain relevant in modern retrieval systems, including those based on large language models (LLMs). However, no single QE method consistently …

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