Pattern Generation for Information Retrieval
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Solution Overview
Problem
The increasing volume of information sources makes it difficult for users to efficiently find and identify information of interest, as existing methods require significant time and computing resources, particularly in resolving references and generating language-specific sentence patterns.
Innovation Solution
A system and method that generates equivalent sentence patterns from a declared pattern, using language-specific combinatorics and semantic context, to determine information of interest from information sources, while minimizing computing resource consumption by employing reference resolution and decomposing complex sentences into simple ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional natural language processing techniques are used for reference resolution, then reference ambiguity is resolved, but computing resource consumption increases significantly
Solution Approach 1:
The patent segments the reference resolution process into distinct phases: anaphor identification, antecedent candidate selection, and resolution. This segmentation allows the system to apply specialized, efficient algorithms to each phase rather than using resource-intensive traditional NLP techniques throughout, thereby reducing overall computing resource consumption while maintaining resolution accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-processing text to identify potential anaphors and their candidates before actual resolution occurs. This preliminary structuring of data enables faster, more efficient resolution in the main processing phase, reducing the computational burden during critical operations.
2Productivity
If a single declared pattern is used to determine information of interest, then processing is simple, but the number of matching word patterns is limited
Solution Approach 1:
The patent implements a pattern generation module that takes a single declared pattern and automatically generates multiple equivalent sentence patterns through syntactic transformations. This allows the system to maintain a simple user interface (single pattern input) while achieving versatile pattern matching coverage (multiple equivalent patterns) for comprehensive information retrieval.
Solution Approach 2:
The system changes linguistic parameters such as word order, voice (active/passive), and syntactic structure to generate equivalent patterns from a single declared pattern. This parameter transformation approach expands pattern matching coverage without requiring users to manually specify multiple patterns, thereby improving both productivity and adaptability.
3Loss of information
If complex sentences are processed directly, then complete information is captured, but processing time and complexity increase
Solution Approach 1:
The patent segments complex sentences into simpler constituent sentences while preserving the semantic relationships between them. This segmentation reduces processing complexity and time for each individual sentence while maintaining complete information capture through the preservation of inter-sentence relationships in the structured representation.
Data Source
AI summary
A method and system is disclosed herein for generating a plurality of equivalent sentence patterns from a declared sentence pattern for a specific language. The declared pattern is fed into a pattern selector. The pattern selector reads a predetermined library of equivalent pattern sets and selects an equivalent pattern set for the declared pattern. The selected equivalent pattern set corresponds to the declared pattern and represents a set of equivalent declared patterns. The set of equivalent declared patterns and the declared pattern are fed to a rules generator. The rules generator outputs executable semantic pattern recognition rules. The reader module, using the generated executable semantic pattern recognition rules, reads the given information source to determine the information of interest.