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vatech.robust.pdf
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🔗 Source: trec.nist.gov
📊 Size: 190 KB
👤 Author: Patrick Fan
⬇️ Downloads: 160

Summary

Ranking functions are crucial for information retrieval systems, but most existing ones are manually designed. This paper proposes a novel ranking function discovery technique called ARRANGER, which uses Genetic Programming (GP) to automatically learn the "best" ranking function for robust retrieval tasks. Experiments on 150 queries from TREC 6, 7, and 8 showed that ARRANGER-improved ranking functions outperformed the Okapi BM25 function by 16% in average precision. With pseudo-relevance feedback and ranking fusion, the performance was further improved by up to 30%. The results demonstrate the effectiveness of ARRANGER in discovering high-performing ranking functions.

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Description

Ranking functions are crucial for information retrieval systems, but most existing ones are manually designed.

Technical Information

  • File Format: PDF
  • File Size: 190 KB
  • Pages: 9
  • Language: EN
  • Author: Patrick Fan
  • Total Downloads: 160
  • Last Updated: 2 hours ago

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