Innovative solutions for document de-duplication
Traditional hashing/fingerprinting methods have difficulty handling semantically similar but literally different documents, and Zerank-1 provides a semantic-level solution.
Implementation of the program:
- Selection of the base document - Use each document as a "query"
- batch matching - Calculate cross-correlation scores with all other documents
- cluster analysis - Documents with scores above 0.85 are considered semantic duplicates
- indexing - Keep the optimal version for each semantic cluster
Optimization Tips:
- Improved computational efficiency using batch prediction
- Coarse-grained classification first reduces computation
- Aided judgment in conjunction with metadata (e.g., release date)
Applicable Scenarios:
It is especially suitable for legal documents, news aggregation, code repositories and other scenarios that require high-precision de-duplication.
This answer comes from the articleZerank-1: A reordering model for improving the precision of search resultsThe
May not be reproduced without permission:AI productivity tools " How to overcome the technical challenges of large-scale document de-duplication?
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