Predicate Parse Refinement via Semantic Knowledge
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Solution Overview
Problem
Current cognitive systems, such as medical diagnosis and decision-support systems, face challenges in creating accurate structured models of passages due to their inability to differentiate concepts using syntactic analysis alone, leading to misinterpretation of logical relationships in natural language content, especially with ambiguous conjunctions and lists.
Innovation Solution
The implementation of mechanisms that apply semantic knowledge to improve predicate parses by performing syntactic analysis, generating candidate meanings, and evaluating these against a semantic knowledge base to determine the correct logical relationships, thereby overriding or maintaining candidate meanings based on semantic support or refutation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If syntactic analysis alone is used to parse natural language content, then the processing speed and simplicity are maintained, but the accuracy of logical modeling deteriorates due to inability to differentiate concepts
Solution Approach 1:
The patent segments the analysis process into two distinct phases: syntactic analysis phase and semantic analysis phase. The syntactic parser first processes the natural language content to identify basic grammatical structures, and then a semantic analysis module selectively processes only the ambiguous decision points identified in the syntactic phase. This segmentation allows the system to maintain processing efficiency while improving accuracy where needed.
Solution Approach 2:
The patent introduces an intermediary component that acts as a bridge between syntactic analysis and semantic analysis. This intermediary identifies ambiguous decision points in the syntactic parse tree and selectively triggers semantic analysis only for those specific points. This intermediary mechanism prevents full semantic analysis from being applied to the entire text, thus maintaining efficiency while improving accuracy.
2Measurement precision
If semantic knowledge base evaluation is performed for all decision points, then the accuracy of meaning determination is improved, but the processing time increases
Solution Approach 1:
The patent applies local quality by differentiating the level of analysis applied to different parts of the text. Instead of applying uniform semantic analysis to all decision points, the system applies semantic knowledge base evaluation only to ambiguous decision points identified by the syntactic parser. This localized application of semantic analysis maintains high accuracy where needed while minimizing processing time for unambiguous portions.
Solution Approach 2:
The patent implements partial action by performing semantic analysis on only a subset of decision points - specifically those identified as ambiguous. Rather than applying semantic evaluation to every decision point in the text, the system selectively applies it where necessary, thus achieving improved accuracy without the full time cost of comprehensive semantic analysis.
3Reliability
If candidate meanings are generated and evaluated against semantic knowledge base, then the correctness of logical relationships is improved, but the complexity of the processing mechanism increases
Solution Approach 1:
The patent applies preliminary action by having the syntactic parser first generate candidate meanings and identify ambiguous decision points before semantic analysis is triggered. This preliminary syntactic analysis creates a structured framework that guides the subsequent semantic evaluation, ensuring that semantic knowledge base queries are focused and efficient rather than exhaustive.
Solution Approach 2:
The patent implements feedback mechanisms where the results of semantic knowledge base evaluation are used to refine and override candidate meanings. The semantic analysis module evaluates characteristics of ambiguous decision points and provides feedback that either confirms or refutes candidate meanings, leading to more reliable logical relationships while maintaining a manageable processing architecture.
Data Source
AI summary
A mechanism is provided for improving predicate parses (or logical representations of a passage) using semantic knowledge. In response to encountering an ambiguous decision point during a syntactic analysis of a portion of natural language content, a candidate meaning of the ambiguous decision point is generated. Characteristics of the ambiguous decision point are evaluated based on a semantic knowledge base to determine a semantic meaning associated with the ambiguous decision point. A determination is made as to whether the semantic meaning supports or refutes the candidate meaning. In response to determining that the semantic meaning refutes the candidate meaning, the candidate meaning of the ambiguous decision point is overridden based on the semantic meaning to include the semantic meaning as a final meaning for the ambiguous decision point. The portion of natural language content is then processed based on the final meaning for the ambiguous decision point.