Document Categorization via Entity Relationship Analysis
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Solution Overview
Problem
Current cognitive computing systems are inadequate in identifying objectionable content within documents, leading to the inclusion of biased or malicious information in corpora, which can result in biased neural network outputs and undesirable processing outcomes.
Innovation Solution
The method involves analyzing entity relationships associated with documents, using natural language processing and sentiment analysis to determine the likelihood of objectionable content, and employing a neural network to recognize patterns in entity-relationship data to categorize documents as likely or unlikely to contain objectionable material.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional content analysis methods are used to categorize documents, then the process is simple and fast, but the ability to detect objectionable content is limited
Solution Approach 1:
The patent segments the document analysis process into multiple independent components: entity identification module, relationship analysis module, and objectionable content detection module. Each module processes specific aspects separately, allowing the system to achieve high detection accuracy through specialized analysis while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces entity relationships as an intermediary layer between the document content and the objectionable content detection. By analyzing relationships between entities (people, organizations, concepts) mentioned in documents, the system gains deeper contextual understanding that improves detection accuracy without requiring direct complex analysis of all content.
2Measurement precision
If entity relationship analysis is performed to improve detection accuracy, then the precision increases, but the processing time increases
Solution Approach 1:
The patent performs preliminary entity identification and relationship extraction before the main objectionable content detection process. By pre-processing documents to extract entities and their relationships in advance, the system reduces the computational burden during the actual detection phase, thereby improving accuracy while minimizing additional processing time.
Solution Approach 2:
The patent applies relationship analysis selectively to specific entities and contexts rather than uniformly to all document content. By focusing computational resources on locally relevant entity relationships that are most indicative of objectionable content, the system achieves high detection accuracy while processing only the necessary portions of documents.
Data Source
AI summary
A document of written content may be obtained. The document may be a candidate for inclusion in a corpus. A first entity associated with the document may be identified. A first discrete entity associated with the first entity may be identified. The relationship associated with the first entity and the first discrete entity may be analyzed. Based on the analyzing, a likelihood that the document contains content that would be detrimental for inclusion in the corpus may be determined.