Semantic Analysis System for Ranking Search Results by Discrepancy
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Solution Overview
Problem
Current computer-based systems lack an effective method for ranking search results that identify discrepancies between self-authored documents and documents authored by others, which is crucial for verifying the consistency of information, especially in applications like insurance claims or job applications.
Innovation Solution
A computer-based semantic analysis system that processes search queries by comparing textual content from self-authored documents with later-authored documents using indicator terms and weighting factors to determine a semantic ranking score, ranking links to documents based on the discrepancy score, and automatically adjusting weighting factors and thresholds for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a semantic analysis system compares textual content from self-authored documents with later-authored documents using indicator terms, then the ability to identify discrepancies is improved, but the device complexity increases
Solution Approach 1:
The system segments the document comparison task into distinct components: extracting indicator terms from documents, calculating discrepancy scores for individual terms, and aggregating these scores into an overall discrepancy assessment. This segmentation allows each component to be optimized independently while maintaining overall system accuracy.
Solution Approach 2:
The system employs adjustable parameters including indicator weighting factors that can be tuned to emphasize or de-emphasize specific indicator terms, and threshold values that can be modified to control the sensitivity of discrepancy detection. These parameter changes enable the system to adapt to different application requirements without fundamental redesign.
2Measurement precision
If the system uses multiple indicator terms with weighting factors to determine discrepancy scores, then the measurement precision of discrepancy detection is improved, but the loss of information increases due to the complexity of processing multiple weighted terms
Solution Approach 1:
The system incorporates feedback mechanisms where the calculated discrepancy scores are fed back into the analysis process, allowing for iterative refinement of indicator weighting factors and threshold values. This feedback loop enables the system to learn from its own performance and continuously improve its discrepancy detection accuracy while managing information complexity.
3Reliability
If the system automatically adjusts weighting factors and thresholds to improve accuracy, then the reliability of discrepancy detection is improved, but the loss of time increases due to the adjustment process
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing optimal weighting factors and threshold values based on training data or historical performance. These pre-computed parameters are then readily available for immediate use in discrepancy detection tasks, eliminating the need for time-consuming real-time adjustments while maintaining high reliability.
Data Source
AI summary
A computer-implemented semantic analysis system for ranking search results has instructions including the steps: receive a query about a first document authored by a person; read in data identifying the first document; read in a second document about the person authored after the first document; identify indicator terms in the second document related to whether or not there is a significant discrepancy between the second document and the first document; read in weighting factors for the identified indicator terms; determine an aggregate weighting factor that decreases with an increasing number of indicator terms found; and calculate a score of how much of a discrepancy there is between the second document and the first document using the weighting factors. When multiple first documents are returned for multiple persons, present the links in order from high score to low score.