NLP Parse Tree Factual Hypothetical Text Annotation
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Solution Overview
Problem
Current natural language processing systems face challenges in accurately distinguishing between actual factual statements and hypothetical descriptions in medical or judicial texts, which can lead to erroneous treatment recommendations or decisions, as they often fail to differentiate between actual events and potential plans or events that have not occurred.
Innovation Solution
The implementation of a method and system that utilizes a natural language processor to analyze textual content by employing dictionary data structures and parse trees, where hypothetical and factual dictionary data structures identify and annotate portions of text as hypothetical or factual, allowing the system to ignore or assign lower weight to hypothetical statements, thereby improving the accuracy of treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language processing systems use traditional methods to analyze textual content, then the system can process text efficiently, but the system cannot accurately distinguish between actual factual statements and hypothetical descriptions
Solution Approach 1:
The patent segments the text analysis process into multiple components: parse tree generation, dictionary data structure matching (separating factual and hypothetical indicators), and annotation processing. This segmentation allows the system to handle complexity systematically while improving accuracy in distinguishing factual from hypothetical statements.
Solution Approach 2:
The patent introduces parse trees and dictionary data structures as intermediary representations between the raw text and the final analysis. These intermediaries facilitate accurate distinction between factual and hypothetical statements by providing structured representations that can be systematically processed and compared against known patterns.
2Reliability
If the system includes all textual content including hypothetical statements, then the system has complete information, but the system generates erroneous treatment recommendations
Solution Approach 1:
The patent extracts and separates hypothetical statements from factual statements through the parse tree and dictionary matching process. By identifying and isolating hypothetical content (marked with specific annotations), the system can exclude this information from treatment recommendation generation while preserving it for analysis, thus improving reliability without complete information loss.
Solution Approach 2:
The patent applies different processing qualities to different parts of the text: factual statements are processed for treatment recommendations with full weight, while hypothetical statements are marked with annotations indicating their speculative nature. This local differentiation ensures reliable recommendations based on factual content while maintaining awareness of hypothetical information where appropriate.
3Measurement precision
If the system processes all text without differentiation, then the processing is simple and fast, but the system cannot differentiate between actual events and potential plans
Solution Approach 1:
The patent performs preliminary parse tree generation and dictionary matching during the text processing stage to pre-identify factual versus hypothetical statements. This preliminary action enables precise differentiation before the final analysis stage, maintaining processing efficiency while achieving high precision in event differentiation.
Data Source
AI summary
A method, system, and computer program product for using a natural language processor includes importing a training text including a plurality of training nodes and having a first marked span that includes some of the plurality of training nodes and a first training trigger, wherein each training node includes a first attribute and a second attribute. Also included are generating a first plurality of training tree tables for each training node with respect to a first training traversal from the first training trigger, each training tree table including the first and second attributes of each training node in the first training traversal, and generating a first plurality of training comparison tables from the first plurality of training tree tables. Each training comparison table is also analyzed to determine if a target node belongs in the first marked span, and labeling each training comparison table accordingly.