Pseudo-Relevance Feedback.pdf

2021103122.pdf
Preview of Pseudo-Relevance Feedback
🔗 Source: inass.org
📊 Size: 343 KB
👤 Author: Kei Eguchi
⬇️ Downloads: 187

Summary

Pseudo-relevance feedback (PRF) combines statistical and semantic term extraction for searching Arabic documents, using TFIDF, BERT, and YAKE to obtain candidate terms, and Borda ranking to ensure Top-K candidate terms are relevant, achieving Precision 5 (P5), P10, Recall, Mean Reciprocal Rate (MRR), and Success Rate (SR@K) of 21%, 14%, 42%, 42%, and 58%, respectively.

Description

Pseudo-relevance feedback (PRF) combines statistical and semantic term extraction for searching Arabic documents, using TFIDF, BERT, and YAKE to obtain...

Technical Information

  • File Format: PDF
  • File Size: 343 KB
  • Pages: 9
  • Language: EN
  • Author: Kei Eguchi
  • Total Downloads: 187
  • Last Updated: 6 days ago

Document Overview

This PDF document about Pseudo-Relevance Feedback provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Pseudo-Relevance Feedback.

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