Visual Parse Tree Bounding Box for Semantic Relationship Identification
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Solution Overview
Problem
Manual identification of semantic relationships between semantic types in natural language content is inefficient, particularly in large datasets like electronic medical records, requiring a method to rapidly and automatically determine these relationships.
Innovation Solution
A system that uses visual recognition to identify semantic relationships by determining parts of speech and semantic types within natural language content, generating a parse tree representation with visually distinguishable nodes, and employing a machine learning model to draw bounding boxes around nodes to classify semantic relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of semantic relationships is used, then accuracy can be maintained, but productivity is significantly reduced
Solution Approach 1:
The patent introduces an intermediary visual representation system that translates natural language content into graphical parse trees with visually encoded semantic information. This intermediary representation enables machine learning models to process and identify semantic relationships automatically while maintaining accuracy, thus resolving the contradiction between manual accuracy and automated speed.
Solution Approach 2:
The patent replaces the manual mechanical process of semantic relationship identification with an automated machine learning system. By substituting human cognitive processing with computational algorithms that analyze visual representations of text structure, the system achieves both high accuracy and high productivity simultaneously.
2Device complexity
If traditional text analysis methods are used, then implementation is simple, but the system cannot effectively handle variability in formatting, writing style, and noise
Solution Approach 1:
The patent transforms one-dimensional text analysis into two-dimensional visual representation analysis by creating parse trees where nodes and edges encode grammatical and semantic relationships. This dimensional transformation allows the system to handle formatting variability, writing style differences, and noise more effectively while maintaining analytical power.
Solution Approach 2:
The patent employs visual characteristics (analogous to color changes) in the parse tree representation to encode different parts of speech and semantic types. This visual encoding system allows the machine learning model to reliably identify semantic relationships regardless of text formatting, style variations, or noise, thereby improving consistency across diverse documents.
Data Source
AI summary
Aspects of the present disclosure relate to identifying semantic relationships. Natural language content is received. A part of speech is determined for respective terms within the natural language content. A semantic type is determined for each of two or more terms within the natural language content. A parse tree representation containing a plurality of nodes is then generated based on the natural language content, each of the plurality of nodes corresponding to at least one term within the natural language content, wherein visual characteristics of respective nodes of the plurality of nodes within the parse tree representation depend on the part of speech and semantic type of the respective terms. A bounding box identifying a semantic relationship is then generated around a set of nodes on the parse tree representation, the set of nodes including the two or more terms.