Semantic Engine Using Self-Describing Fragments
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing natural language processing systems face inefficiencies due to complex semantic rules and high computational intensity, particularly when determining word meanings and syntactic relationships, leading to unworkable solutions in many environments.
Innovation Solution
A method and apparatus utilizing a semantic engine with a hierarchical schema to analyze input text, generating self-describing fragments that reduce storage needs and computational load by mapping input text to predefined semantic structures, allowing for efficient identification of semantic solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic rules are applied directly to individual words to identify semantics, then semantic identification can be performed, but the number of rules becomes very large and complex making the system unworkable
Solution Approach 1:
The patent introduces syntactic categories (parts of speech, phrases, clauses) as intermediary structures between raw words and semantic meanings. Instead of directly mapping words to semantics with complex rules, the system first performs syntactic analysis to create structured representations, then applies simpler semantic rules to these structures. This intermediary layer reduces the complexity of semantic rules while maintaining identification accuracy.
Solution Approach 2:
The patent segments the natural language processing task into distinct phases: lexical analysis, syntactic analysis, and semantic analysis. By dividing the processing into separate stages, each handling specific aspects of language understanding, the system avoids the need for monolithic complex semantic rules and instead uses modular, simpler rules at each stage.
2Device complexity
If syntactic analysis is performed to identify parts of speech and relationships before semantic analysis, then the number of semantic rules is reduced, but storage space requirements remain large and processing becomes computationally intensive
Solution Approach 1:
The patent creates a universal syntactic representation framework that can handle multiple language structures and semantic relationships through a single set of syntactic categories and rules. This universal framework reduces the need for separate storage for different syntactic patterns, thereby reducing overall storage requirements while maintaining comprehensive language understanding capabilities.
3Measurement precision
If a two-stage evaluation process with separate syntactic parse is used, then semantic analysis can be performed systematically, but the process becomes computationally intensive and slow
Solution Approach 1:
The patent merges the syntactic analysis and semantic analysis stages into a more integrated process where syntactic structures are directly utilized for semantic interpretation without requiring separate sequential passes. This merging reduces the computational overhead of repeated processing and improves overall processing speed while maintaining systematic analysis through the structured syntactic representations.
Data Source
AI summary
A method and apparatus for identifying a semantic structure from text includes processing the input text to identify self-describing fragments of the input text based on a hierarchical schema defining a domain with at least one top-level node and child nodes. Each identified self-describing fragment includes hierarchical context of a portion of the input text. A semantic structure is provided based on the identified self-describing fragments.