Keyword Stuffing Detection Using Block-Level Character Prediction
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Solution Overview
Problem
The accuracy of information retrieval is degraded due to keyword stuffing, which affects the relevance calculation in retrieval-augmented generation (RAG) systems.
Innovation Solution
A prediction program that allocates input character strings to blocks, predicts the probability of keyword stuffing using a feature amount of each character, and detects the center and length of keyword segments through a detector, with a correction process to ensure accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword stuffing detection is performed using only sentence details in RAG, then the system is simple to operate, but the detection accuracy is degraded
Solution Approach 1:
The input character string is divided into multiple blocks, and keyword stuffing detection is performed for each block independently. This segmentation allows the system to handle long keyword segments effectively while maintaining manageable complexity through modular processing.
Solution Approach 2:
The patent transitions from detecting keyword stuffing at the sentence level to the block level, adding a dimensional aspect to the detection process. This enables more granular analysis and improves detection accuracy by examining local patterns within blocks rather than treating the entire sentence as a single unit.
2Measurement precision
If keyword stuffing detection is performed at the character level, then detection precision is improved, but the processing time increases
Solution Approach 1:
The character string is segmented into blocks of manageable size, allowing precise character-level analysis within each block while avoiding the computational burden of processing the entire string at once. This segmentation enables efficient parallel processing and reduces overall processing time.
Solution Approach 2:
The patent applies keyword stuffing detection to each block independently rather than requiring complete analysis of the entire character string before making detections. This partial action approach allows for faster incremental processing and early detection of keyword stuffing patterns.
Data Source
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AI summary
A prediction program that causes a computer to execute a process including allocating an input first character string to a block that satisfies a predetermined condition, predicting, by using a feature amount of each character of a second character string in each block and a detector configured to detect keyword stuffing, a probability that keyword stuffing is present in the second character string, and predicting a center and a length of a keyword segment in the second character string when the probability is a predetermined threshold or more.