Subtext Search Using Synonym-Enriched Predicative Phrases
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Solution Overview
Problem
Existing information search systems, such as internet searches and database queries, often return irrelevant or nonsensical results due to the lack of explicit contextual information, leading to inefficient matching of textual items and collections of information.
Innovation Solution
The system enriches information by extracting and utilizing subtext, which includes implicit information from related texts, images, and symbols, to improve search accuracy by analyzing and matching the contextual sense of textual passages, using techniques like linguistic pattern extraction, lexical noise reduction, and synonym substitution to create a more comprehensive metadata for better compatibility testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword-based search is used, then search speed is fast, but search accuracy and relevance deteriorate due to lack of contextual understanding
Solution Approach 1:
The system performs preliminary extraction of linguistic patterns, predicative phrases, and subtext from text before the actual search query is executed. User profiles and document profiles are pre-computed with extracted linguistic features, enabling faster and more accurate matching during search without real-time processing complexity
Solution Approach 2:
The patent introduces linguistic patterns and predicative phrases as intermediary representations between raw text and search queries. These intermediaries capture the contextual meaning and subtext of documents, allowing the search system to match semantic intent rather than just keywords, thereby improving accuracy without requiring complex real-time analysis
2Loss of information
If explicit text matching is used, then processing is simple, but implicit meaning and subtext are lost
Solution Approach 1:
The patent segments text into distinct linguistic components including predicative phrases, linguistic patterns, and subtext elements. Each segment is processed and stored separately in user profiles and document profiles, allowing the system to retrieve and match specific semantic components without reprocessing entire texts, thus preserving information while maintaining efficiency
Solution Approach 2:
The system transforms text from its original form into multiple parameter representations including frequency of occurrence, weight of predicative phrases, and subtextual meanings. These parameter changes enable the search system to query and match documents based on semantic attributes rather than raw text, preserving implicit meaning while enabling efficient processing through structured data comparison
Data Source
AI summary
A method and system for automatically, without the necessity of user intervention, creating subtext from textual information regarding text and/or images and/or symbols, etc. and using the subtext to associate by sense passages of the textual information with each other and/or with passages related to search queries.