Text Mining System with Implication Graph for Semantic Analysis
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Solution Overview
Problem
Syntactic dependency-based inter-word networks fail to allow for direct understanding of text content, as they do not accurately represent the relationships between sentences with different word orders, requiring human interpretation to understand specific text meanings.
Innovation Solution
A text mining system that acquires synonym clusters and generates an implication graph with directed edges to represent relationships between synonymous texts, enabling direct analysis of text content without altering the original text form.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If syntactic dependency relationships are used to analyze text, then the analysis structure is established, but the text content cannot be directly understood
Solution Approach 1:
The patent creates a semantic network that copies and represents the meaning relationships between words and phrases in the text. Instead of relying on syntactic dependencies alone, the system builds a parallel semantic representation that preserves the actual content meaning, allowing direct understanding of text content while maintaining analytical structure.
Solution Approach 2:
The patent introduces semantic relationships as an intermediary layer between syntactic structure and content understanding. The semantic network acts as a mediator that translates syntactic dependencies into meaningful content relationships, enabling the system to bridge the gap between structural analysis and content comprehension.
2Device complexity
If words are arranged into predefined categories based on syntactic dependencies, then the analysis framework is organized, but specific text meanings are lost
Solution Approach 1:
The patent applies local quality by creating specific semantic relationships for different word pairs based on their contextual meanings rather than applying uniform syntactic categorization. Each edge in the semantic network carries specific semantic information tailored to the local context, preserving specific text meanings while maintaining overall framework organization.
3Productivity
If syntactic dependency networks are used for text analysis, then the inter-word relationships are mapped, but direct content understanding requires human interpretation
Solution Approach 1:
The patent replaces the mechanical human interpretation process with an automated semantic network construction system. The system automatically builds and analyzes semantic relationships between text elements, substituting the need for human rearrangement and interpretation of syntactic dependencies with automated semantic processing that directly produces understandable content analysis.
Data Source
AI summary
The present invention is a text mining system comprising a synonym cluster acquiring section configured to acquire synonym clusters from texts in text data to be analyzed, the synonym clusters each being a collection of synonymous texts, an implication relationship acquiring section configured to acquire implication relationships among the synonym clusters, and an implication graph generating section configured to generate an implication graph including vertices of synonym clusters and directed edges each indicating a direction from an implied synonym cluster to an implying synonym cluster from the implication relationships among the synonym clusters.