Cross-Layer Pattern Recognition for Text Semantic Phenomena
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Solution Overview
Problem
Existing automated machine learning systems face challenges in identifying target semantic phenomena in human-readable text, as relationships between words can be elusive and non-intuitive, making it difficult to define and recognize using code.
Innovation Solution
A computer-implemented method that extracts feature-values from words in training text fragments, statistically analyzes these values to identify cross-layer patterns, and uses a trained statistical classifier to match or correlate new text fragments, enabling the identification of target semantic phenomena by recognizing common patterns across multiple layers such as semantic, syntactic, and domain knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated machine learning systems use code to identify target semantic phenomena, then processing efficiency is improved, but the ability to recognize elusive and non-intuitive word relationships deteriorates
Solution Approach 1:
The system segments the identification task into multiple layers of features (semantic features, syntactic features, domain knowledge features) that can be independently extracted and combined. This allows the system to process text efficiently through structured feature extraction while maintaining recognition accuracy by considering multiple aspects of word relationships simultaneously.
Solution Approach 2:
The system introduces cross-layer patterns as intermediary representations that bridge the gap between different feature layers. These patterns serve as mediators that capture elusive word relationships by combining features from multiple layers, enabling the system to recognize non-intuitive relationships while maintaining processing efficiency.
2Reliability
If manual human definitions are used to identify target semantic phenomena, then recognition accuracy is improved, but resource requirements and processing time increase
Solution Approach 1:
The system enables automated generation of cross-layer patterns through statistical analysis of training data, eliminating the need for manual human definitions. The system serves itself by learning patterns directly from data, reducing resource requirements and processing time while maintaining recognition accuracy through multi-layer feature integration.
Solution Approach 2:
The system changes the parameters of pattern representation by using cross-layer patterns that combine multiple feature dimensions. This allows the system to achieve high recognition accuracy without manual definitions, as the cross-layer patterns capture complex word relationships through parameter combinations rather than explicit manual rules.
3Measurement precision
If multi-layered feature extraction is applied, then pattern recognition capability is improved, but system complexity increases
Solution Approach 1:
The system segments the complex pattern recognition task into distinct feature layers (semantic, syntactic, domain knowledge), each handled by specialized extraction modules. This segmentation improves pattern recognition capability by capturing multiple aspects of word relationships while managing system complexity through modular organization of extraction processes.
Solution Approach 2:
The cross-layer patterns serve as universal representations that integrate features from multiple layers. This multi-functionality allows the system to achieve high pattern recognition capability without proportionally increasing system complexity, as the same cross-layer pattern framework handles integration across all feature layers.
Data Source
AI summary
There is provided a computer-implemented method for outputting one or more cross-layer patterns to identify a target semantic phenomenon in text, the method comprising: extracting, for each word of at least some words of each training text fragment of training text fragments designated as representing a target semantic phenomenon, feature-values defined by respective layers; statistically analyzing the feature-values identified for the training text fragments to identify one or more cross-layer patterns comprising layers representing a common pattern for the training text fragments, the common cross-layer pattern defining one or more feature-values of a respective layer of one or more words and at least another feature-value of another respective layer of another word; and outputting the identified cross-layer pattern(s) for identifying a text fragment representing the target semantic phenomenon.