Clause Analyzer for Hierarchical Dependency Parsing
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Solution Overview
Problem
Current natural language data processing systems face challenges in accurately analyzing and interpreting complex sentence structures, particularly in identifying and hierarchically relating clauses within human language text, which affects the extraction of meaningful information and sentiment analysis.
Innovation Solution
A computer-program product and method that utilizes a clause analyzer to receive text data, generate dependency requests, and determine dependency information for tokens within sentences, creating a hierarchy of clauses to identify main, subordinate, and interrupted clauses, and classify tokens as heads or members, thereby generating a new data set with location identifiers for each token in the hierarchy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language data processing systems use traditional methods to analyze sentences, then the processing is simpler and faster, but the accuracy in identifying and hierarchically relating clauses deteriorates
Solution Approach 1:
The system segments the sentence analysis process into distinct components: tokenization (breaking text into tokens), dependency parsing (identifying grammatical relationships between tokens), and clause hierarchy construction (organizing clauses in a hierarchical structure). This segmentation allows each component to be optimized independently while improving overall clause identification accuracy.
Solution Approach 2:
The patent introduces intermediate data structures and processing stages as mediators between the input text and final clause analysis. Dependency trees and clause hierarchy structures serve as intermediary representations that bridge the gap between simple token sequences and complex clause relationships, enabling more accurate analysis without requiring the entire system to be overly complex.
2Loss of information
If the system performs detailed dependency analysis and hierarchy construction, then the information extraction quality improves, but the processing time increases
Solution Approach 1:
The system performs preliminary tokenization and dependency parsing before constructing the full clause hierarchy. By preparing intermediate representations in advance, the system reduces the computational burden during the final clause identification and information extraction stages, thereby reducing overall processing time while maintaining information quality.
Solution Approach 2:
The patent applies different levels of analysis depth to different parts of the sentence based on their importance. Critical clauses and tokens receive more detailed dependency analysis, while less important elements undergo lighter processing. This local quality approach ensures high information extraction quality for key elements while reducing processing time for the overall sentence.
3Measurement precision
If the system identifies all clause types and relationships, then the sentiment analysis accuracy improves, but the computational resources required increase
Solution Approach 1:
The system dynamically adjusts analysis parameters such as the depth of dependency parsing and the level of clause hierarchy detail based on the complexity of the input text and the specific analysis task. For sentiment analysis, the system can increase the depth of clause relationship identification, while for simpler tasks, it reduces computational parameters to conserve resources.
Solution Approach 2:
The patent implements a tiered approach where the system performs essential clause identification for all texts, and optional deeper analysis of clause relationships and types only when needed for specific tasks like sentiment analysis. This partial action approach ensures baseline accuracy while allowing resource-intensive detailed analysis to be applied selectively rather than universally.
Data Source
AI summary
A human language analyzer receives, at the human language analyzer, text data representing information in a human language. The human language analyzer receives a computer command for identifying a text data component of the text data. The computer command comprises at least two requirements for the text data component. The human language analyzer, responsive to identifying that the first requirement and the second requirement are met, locates the text data component from one of two clauses. A clause analyzer receives a clause request to locate clauses within text data representing information in a human language. The clause analyzer receives, responsive to a dependency request, token information in a token data set. The clause analyzer determines a location for each clause of the sentence portion in a hierarchy of clauses. The clause analyzer generates and outputs a new data set based on the token data set and the hierarchy of clauses.