Semantic Proximity Evaluation for Free-Form Text Matching
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Solution Overview
Problem
Conventional natural language systems face challenges in distinguishing relevant documents from irrelevant ones, particularly when using keyword indexing or reference patterns, as they require precise syntax and trained contributors, placing a burden on users and knowledge contributors.
Innovation Solution
A system that evaluates semantic proximity between free-form text entries and candidate requests, using a multi-step analysis involving compiler and transformer processes to compile and transform text into rephrased objects, allowing for effective reference patterns without strict syntax, and utilizing rephrase rules and symbolic representations of synonyms to enhance matching accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If keyword indexing is used to retrieve relevant documents, then the system can operate with simple implementation, but it cannot distinguish relevant documents from documents that share a few words with the user request
Solution Approach 1:
The system transforms the matching parameters from simple keyword presence to semantic similarity metrics. It uses natural language processing to convert both queries and documents into semantic representations, then calculates similarity based on multiple parameters including semantic proximity, structural similarity, and contextual relevance, thereby improving discrimination accuracy while maintaining implementation feasibility through modular architecture
2Measurement precision
If reference patterns with specific syntax are used to improve matching accuracy, then relevant documents can be identified more precisely, but the burden on knowledge contributors to program accurate patterns increases significantly
Solution Approach 1:
The system enables knowledge contributors to provide free-form natural language reference text without requiring training in complex pattern syntax. The automated semantic analysis engine processes these natural language inputs, automatically generates appropriate reference patterns, and performs matching without human intervention in the pattern creation process, thereby maintaining high accuracy while eliminating the burden on contributors
Solution Approach 2:
The system introduces an automated natural language processing intermediary that sits between the contributor's free-form text input and the pattern matching engine. This intermediary automatically transforms unstructured natural language into structured reference patterns, shielding contributors from syntax complexity while ensuring accurate pattern generation for precise matching
3Ease of operation
If free-form text entries are used instead of structured patterns, then the burden on contributors is reduced, but the system loses the ability to achieve practical matching results without trained contributors
Solution Approach 1:
The system replaces the mechanical syntax-based pattern matching system with a semantic-based natural language processing system. Instead of requiring structured patterns with specific syntax, it uses automated NLP techniques including semantic analysis, entity recognition, and similarity calculation to process free-form text entries, thereby maintaining matching effectiveness while dramatically improving ease of use for contributors
Data Source
AI summary
Methods and apparatus to evaluate the semantic proximity between reference free-form text entry and a candidate free-form text request.