Split-Phrase Tumbling-Frequency Phrase-Chain Parser for NLP
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Solution Overview
Problem
Existing natural language processing systems, particularly those using parsing approaches, face inefficiencies in accommodating user variability and sentence length, leading to decreased accuracy and increased processing power requirements, making them ineffective for handling complex or lengthy sentences, especially in domains like scientific journals.
Innovation Solution
The development of a Split-Phrase Tumbling-Frequency Phrase-Chain Parser that identifies a finite set of 'split-phrase chains' by separating phrases into alpha and beta sub-phrases based on non-auxiliary verbs and nouns, allowing for pre-encoding of semantic meaning, thereby simplifying and accelerating natural language processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If parsing-based natural language processing systems are used to accommodate user variability and sentence length, then the systems can handle diverse expressions, but the processing power requirements increase and accuracy decreases for complex or lengthy sentences
Solution Approach 1:
The patent segments phrases into two sub-phrases: alpha sub-phrases containing functional components and beta sub-phrases containing specific content. This segmentation allows the system to handle diverse expressions efficiently by processing only the functional parts through the finite phrase chain set, while treating content parts as variables, thus resolving the contradiction between adaptability and processing efficiency.
2Adaptability or versatility
If parsing-based natural language processing systems are used to accommodate user variability and sentence length, then the systems can handle diverse expressions, but the processing power requirements increase
Solution Approach 1:
By dividing phrases into alpha and beta sub-phrases, the system reduces processing power requirements. The alpha sub-phrases (functional components) are processed using the finite phrase chain set, while beta sub-phrases (content) are handled as parameters, significantly reducing computational energy while maintaining adaptability to diverse expressions.
3Measurement precision
If traditional parsing methods are used, then comprehensive language analysis is achieved, but the processing is slower and less accurate for semantic-level understanding
Solution Approach 1:
The patent improves semantic-level accuracy and processing speed by segmenting phrases into alpha sub-phrases (functional) and beta sub-phrases (content). The finite phrase chain set processes alpha sub-phrases with high accuracy, while beta sub-phrases are handled as parameters, achieving both precision and speed that traditional parsing methods cannot attain.
Data Source
AI summary
Of the four primary approaches to processing language by computer, only the parsing approach considers the semantic and syntactic components from the start. In doing so, however, the required resources expand rapidly as the scope of the language processed increases. And as that scope increases, the performance of parsing systems decreases. A natural language processor uses a tumbling-frequency phrase chain parser as described herein which circumvents this resource-intensive step in parsing, while quickly and almost effortlessly arriving at the next step in natural-language processing with far more accurate results involving a partitioning dictionary and phrase chains.