Document Fingerprinting for Parallel Text Identification
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Solution Overview
Problem
Conventional text-to-text applications face significant computational challenges in identifying mutual translations across multiple documents due to the need for comparing all document pairs, leading to extreme computation times, especially when dealing with large datasets like the internet, where not all web documents follow specific URL patterns.
Innovation Solution
The system employs a similarity measure that generates a reduced-size document fingerprint for each document, using probabilistic shuffling and comparison techniques to efficiently identify matches, scaling with n*log n rather than n^2, and utilizes focused crawling and machine learning to find parallel documents across languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all document pairs are compared using conventional systems to identify parallel documents, then identification accuracy is improved, but computation time increases dramatically (scales with n^2)
Solution Approach 1:
The patent segments the document comparison process into two stages: first computing document signatures (fingerprints) that capture essential content features, then comparing only these compressed representations rather than full documents. This segmentation reduces the comparison complexity from n^2 to n*log n while maintaining identification accuracy.
Solution Approach 2:
The patent extracts the most important content features from documents and encodes them into compact signatures. By taking out only the essential information needed for identification and discarding redundant content, the system achieves fast comparison without sacrificing accuracy in identifying parallel documents.
2Loss of time
If Resnik's URL-based method is used to identify parallel documents, then computation time is reduced, but adaptability to documents without specific URL patterns deteriorates
Solution Approach 1:
The patent creates a universal document signature system that works across all document types and formats, not just web pages with specific URL patterns. The signature method is format-agnostic and can process any text document, making the system universally applicable to diverse document collections while maintaining efficient n*log n performance.
3Loss of time
If documents are compared using reduced-size fingerprints, then computation time is reduced (scales with n*log n), but comparison precision may deteriorate
Solution Approach 1:
The patent applies local quality by creating signatures that capture the most discriminative local features of documents - such as word frequency distributions, n-gram patterns, and content-specific markers - while discarding less informative global characteristics. This selective feature extraction maintains high comparison precision despite using compressed representations.
Data Source
AI summary
A training system for text to text application. The training system finds groups of documents, and identifies automatically similar documents in the groups which are similar. The automatically identified documents can then be used for training of the text to text application. The comparison uses reduced size versions of the documents in order to minimize the amount of processing.