Rolling Window Text Representation via Token Fingerprints
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Solution Overview
Problem
Existing text representation methods using sliding windows face challenges in efficiently computing and updating window representations without hash computation over the full text, especially when dealing with large corpora and diverse domains.
Innovation Solution
A system that computes token-level fingerprints for each token in a received window of text and updates the window representation in a rolling scheme using MinHash and SimHash techniques, allowing for efficient fuzzy and semantic matching without requiring hash computations over the full text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hash computation is performed over the full text to create window representations, then the accuracy and completeness of text representation is improved, but the computational time and processing speed deteriorate
Solution Approach 1:
The patent divides the full text into smaller sliding windows of fixed size, computing representations for each window independently. This segmentation allows the system to process text in manageable chunks rather than computing over the entire corpus at once, significantly reducing computational time while maintaining representation quality for each local region.
Solution Approach 2:
The patent pre-computes and stores token-level fingerprints for all tokens in the corpus before performing window representation computation. This preliminary action eliminates the need to recompute token fingerprints during window sliding, reducing redundant calculations and improving overall processing speed when generating window representations.
2Loss of information
If sliding window size is increased to capture more context, then the semantic completeness of text representation is improved, but the computational complexity and memory usage worsen
Solution Approach 1:
The patent implements a dynamic sliding window mechanism that can adjust its position and size adaptively. The window slides through the text with configurable step sizes, allowing the system to capture varying amounts of context depending on the specific processing needs. This dynamic approach enables flexible control over the balance between context capture and computational complexity.
Solution Approach 2:
The patent allows modification of key parameters including window size, step size, and token fingerprint dimensions. By adjusting these parameters, users can optimize the system for different scenarios - larger windows for semantic completeness, smaller windows for efficiency, or intermediate values for balanced performance. This parameter flexibility directly addresses the trade-off between semantic completeness and computational complexity.
3Measurement precision
If token-level fingerprints are computed for all tokens in the corpus, then the quality of window representations is improved, but the memory requirements and storage costs worsen
Solution Approach 1:
The patent uses compact token-level fingerprints as simplified representations of tokens, which serve as disposable intermediate structures during window representation computation. These fingerprints are much smaller than full token embeddings or original text, reducing memory requirements while maintaining sufficient information for accurate window representation. The system computes fingerprints on-demand and discards them after use, avoiding long-term storage of large data structures.
Data Source
AI summary
An example system includes a processor to compute a token-level fingerprint for each of a number of tokens in a received window of text. The processor can compute a window representation for a window of text based on the token-level fingerprints. The processor can also update the window representation in a rolling scheme when sliding the window of text.