Towards Explaining Query Expansion Performance in Information Retrieval

Key points

• The first is the concept of an Ideal Expanded Query (IEQ)--a hypothetical query that maximizes retrieval effectiveness with a downstream BM25 retrieval model.1
• We develop a separability measure and practical formulations to approximate the IEQ and investigate how these factors relate to retrieval effectiveness.1
• In particular, we find that expanded queries that are closer to the ideal expanded query tend to achieve higher retrieval effectiveness.1
• We further show that the separability of relevant and non-relevant documents provides a complementary perspective for understanding QE performance.1

Sources (1)

• [1]Towards Explaining Query Expansion Performance in Information Retrieval
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 09:21 AM

  Query Expansion (QE) techniques have long been widely used in Information Retrieval (IR) to address the vocabulary mismatch problem.

  The first is the concept of an Ideal Expanded Query (IEQ)--a hypothetical query that maximizes retrieval effectiveness with a downstream BM25 retrieval model.

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