Weighted Parsing Scoring for Context Determination
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Solution Overview
Problem
Natural language processing systems face challenges in understanding context-dependent meanings and efficiently determining relationships between entities, particularly due to the need for domain-specific rules and scalability issues in processing large corpora of text.
Innovation Solution
A method utilizing linguistic and lexical features to detect natural language entities, run parsing algorithms to determine relationships, and assign scores based on context, allowing for domain-independent processing without requiring specific rules for different domains, enabling applications like co-reference resolution, summarization, and question answering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If domain-specific rules are used for natural language processing, then understanding accuracy in specific domains is improved, but system complexity and scalability deteriorate
Solution Approach 1:
The patent implements a universal context determination system that uses parsing algorithms and weighted scoring to identify contextual relationships between entities across any domain. The system employs generic linguistic and lexical features rather than domain-specific rules, allowing the same framework to handle medical, legal, technical, or casual conversation domains without modification. This universal approach resolves the contradiction by maintaining high context understanding accuracy through sophisticated algorithmic analysis while avoiding the complexity of maintaining separate rule sets for each domain.
Solution Approach 2:
The system dynamically adjusts weighting parameters for different parsing algorithms and contextual features based on the specific text being analyzed. Rather than using fixed domain-specific rules, the system modifies parameters such as algorithm weights, distance thresholds, and feature importance scores to optimize context determination for any given text. This parameter-based adaptability allows the system to achieve domain-specific accuracy without requiring domain-specific rule implementations.
2Measurement precision
If multiple parsing algorithms are run to determine contextual relationships, then context determination accuracy is improved, but processing time increases
Solution Approach 1:
The system implements a tiered processing approach where it first applies a subset of parsing algorithms and weighting schemes to identify likely contextual relationships. Only for cases where the initial analysis is ambiguous or the stakes are high does the system invoke additional parsing algorithms and more comprehensive analysis. This partial action strategy maintains high accuracy for critical determinations while reducing average processing time by avoiding exhaustive analysis for all text pairs.
Solution Approach 2:
The context determination process is segmented into multiple independent stages: entity detection, relationship identification through parsing algorithms, scoring of potential relationships, and final threshold-based determination. Each stage can be independently optimized and executed, allowing the system to process text efficiently by completing simple cases quickly while allocating more computational resources only when necessary for ambiguous cases.
3Measurement precision
If a comprehensive scoring system is used to evaluate contextual relationships, then relationship identification accuracy is improved, but computational complexity increases
Solution Approach 1:
The scoring system dynamically adjusts the importance weights of different contextual features based on the specific text being analyzed. Rather than using a static comprehensive scoring model that evaluates all possible features equally, the system adapts the scoring criteria to emphasize the most relevant features for each particular text pair. This dynamic weighting reduces computational complexity by focusing calculations on the most informative features while maintaining high accuracy through adaptive feature selection.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for natural language processing is provided. The present invention may include detecting natural language entities, and running parsing algorithms on the natural language entities to determine the relationship between said natural language entities. The present invention may further comprise assigning, by the parsing algorithms, initial scores to detected natural language entities based on the relationship between said natural language entities; choosing a final score for plurality of natural language entities; and comparing the final score against a threshold to determine whether the natural language entities are within the same context.