Multi-magnitudinal Vector Resolution via Source Features
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Solution Overview
Problem
Existing vector comparison methods face challenges in effectively resolving multi-magnitudinal target vectors to single-magnitude source vectors, particularly in natural language processing and medical coding, where parse items need to be compared against known vectors to assign appropriate codes, and existing methods struggle with selecting the appropriate magnitudes based on various features.
Innovation Solution
The implementation of a multi-magnitudinal vector system that processes target vectors with multiple magnitudes by selecting one magnitude based on features of the source vector, such as morphological, syntactic characteristics, proximity, frequency, and context, to resolve each dimension to a single magnitude, facilitating accurate comparisons and code assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple magnitudes are assigned to target vector dimensions to improve comparison accuracy, then measurement precision is improved, but device complexity increases due to the need to manage and resolve multiple magnitudes
Solution Approach 1:
The system changes the parameter of magnitude assignment by allowing multiple magnitudes to be assigned to target vector dimensions based on source vector features. This enables the system to adapt the magnitude parameter dynamically according to the characteristics of the source vector, thereby improving comparison precision without requiring a completely new system architecture
Solution Approach 2:
The system performs preliminary action by pre-assigning multiple possible magnitudes to target vector dimensions before the actual comparison process. This allows the comparison algorithm to select from pre-prepared magnitude options based on source vector features, improving accuracy while avoiding the complexity of calculating multiple magnitudes during real-time comparison
2Adaptability or versatility
If multiple magnitudes are assigned to target vector dimensions to capture different features, then adaptability is improved, but difficulty of detecting and measuring increases due to the complexity of selecting appropriate magnitudes
Solution Approach 1:
The system changes the parameter of magnitude assignment by allowing multiple magnitudes to be assigned to target vector dimensions based on source vector features. This enables the system to adapt the magnitude parameter dynamically according to the characteristics of the source vector, thereby improving comparison precision without requiring a completely new system architecture
Solution Approach 2:
The system applies self-service by using features from the source vector itself to determine which magnitude to select for the target vector comparison. The source vector's own characteristics (such as term frequency, position, or semantic properties) automatically guide the magnitude selection process, eliminating the need for external complex decision-making mechanisms
Data Source
AI summary
Methods, systems and computer program products for resolving multiple magnitudes assigned to a target vector are disclosed. A target vector that includes one or more target vector dimensions is received. One of the target vector dimensions is processed to determine a total number of magnitudes assigned to the processed target vector dimension. Also, a source vector that includes one or more source vector dimensions is received. The received source vector is processed to determine a total number of features associated with the source vector. When it is detected that the total number of magnitudes assigned to the processed target vector dimension exceeds one, one of the assigned magnitudes is selected based on one of the determined features associated with the source vector.