Text Classification Using Complementary Granularity Levels
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Solution Overview
Problem
Current text analysis methods are time-consuming and inefficient, requiring screening at a single granularity level, which limits their effectiveness in accurately identifying objects of interest within text data.
Innovation Solution
A method and system for text classification at multiple levels of granularity, allowing for the selection of an optimized granularity level based on comparative selection values, confidence scores, and quantifiable benefits and costs, to accurately identify objects of interest while minimizing errors and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text analysis is performed at a single granularity level, then the analysis process is simple, but the accuracy and effectiveness of identifying objects of interest is limited
Solution Approach 1:
The patent divides text analysis into multiple granularity levels (coarse-grained and fine-grained), allowing the system to segment the analysis process into different stages. This enables the system to first perform broad categorization at a coarse level, then apply more detailed analysis only where needed, thereby improving accuracy without uniformly increasing complexity across all text processing.
Solution Approach 2:
The system dynamically adjusts the granularity level based on the specific text segment being analyzed. By making the granularity level adjustable and context-dependent, the system can optimize between simplicity and accuracy for different types of text, improving overall effectiveness while avoiding unnecessary complexity in cases where it is not required.
2Measurement precision
If text analysis is performed at multiple granularity levels, then the accuracy of identifying objects of interest improves, but the computational time and resources increase
Solution Approach 1:
By segmenting the text analysis into multiple granularity levels, the system can process different portions of text at appropriate detail levels. This segmentation allows the system to achieve high accuracy for complex text segments while using simpler, faster processing for straightforward segments, thereby reducing overall computational time compared to uniformly applying fine-grained analysis to all text.
Solution Approach 2:
The system applies fine-grained analysis only partially, specifically to text segments where it is most beneficial, rather than applying it excessively to all text. This selective application of detailed analysis maintains high accuracy where needed while minimizing the computational overhead associated with fine-grained processing across the entire text corpus.
3Measurement precision
If text analysis is performed at multiple granularity levels, then the accuracy of identifying objects of interest improves, but the computational resources required increase
Solution Approach 1:
Segmenting the analysis into multiple granularity levels allows the system to allocate computational resources more efficiently. By processing text at coarse granularity first and only applying computationally intensive fine-grained analysis where necessary, the system reduces overall resource consumption while maintaining high accuracy for critical classifications.
Solution Approach 2:
The system applies computationally expensive fine-grained analysis only partially, to specific text segments where high accuracy is most valuable, rather than applying it to all text uniformly. This partial application significantly reduces total computational resource requirements while preserving accuracy where it matters most.
Data Source
AI summary
A processor may receive a text segment. The processor may analyze the text segment at a plurality of granularity levels wherein each of the plurality of granularity levels has a comparative selection value for identifying one or more objects of interest within the text segment. The processor may select an optimized granularity level with an optimum comparative selection value. The processor may identify the one or more objects of interest within the text segment. The processor may display the one or more objects of interest to a user.