Neural Network Concept Disambiguation via Contextual Validation
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Solution Overview
Problem
Existing natural language processing (NLP) methods for disambiguating concepts in natural language inputs face challenges in accurately determining the valid sense of a concept based on surrounding context, often leading to incorrect interpretations due to lack of contextual examination and reliance on large training sets that may not cover all senses.
Innovation Solution
A system that utilizes neural networks to analyze text and determine valid natural language representations of concepts by applying word embedding techniques and contextual validation, allowing for iterative hypothesis selection and validation within the context of the input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing NLP methods are used for concept disambiguation, then processing can be performed with simpler methods, but accuracy is reduced due to lack of contextual examination
Solution Approach 1:
The patent segments the concept disambiguation process into distinct stages: hypothesis generation from multiple natural language representations, contextual analysis through neural network processing, and validation through scoring mechanisms. This segmentation allows each component to specialize in specific tasks, improving overall accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by generating multiple hypotheses and examining contextual clues before finalizing the disambiguation result. The neural network analyzes surrounding text, identifies contextual patterns, and pre-evaluates potential meanings before selecting the final interpretation, ensuring accurate contextual examination without requiring overly complex post-processing.
2Adaptability or versatility
If large training sets are used to cover all concept senses, then coverage is improved, but the system becomes less reliable when training data is insufficient
Solution Approach 1:
The patent implements feedback mechanisms where the neural network continuously evaluates hypotheses against contextual evidence and adjusts probability scores based on how well each hypothesis fits the surrounding text. This feedback loop allows the system to reliably determine the most appropriate concept sense even when training data coverage is limited, as the contextual feedback provides additional validation beyond pre-trained knowledge.
Solution Approach 2:
The system changes parameters dynamically by adjusting the confidence thresholds and weighting factors based on the quality and quantity of available training data. When training sets are small, the system increases reliance on contextual analysis parameters and decreases dependence on pre-trained sense distributions, maintaining reliability across varying data availability conditions.
3Measurement precision
If contextual analysis is performed thoroughly, then disambiguation accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by analyzing only the most relevant contextual clues rather than processing entire documents. The neural network identifies and focuses on key phrases, surrounding words, and local contextual patterns that are most indicative of the intended meaning, achieving high accuracy without the computational overhead of exhaustive contextual analysis.
Solution Approach 2:
The system implements skipping mechanisms by rapidly processing low-information contextual elements and focusing computational resources on high-value analysis. The neural network quickly evaluates obvious contextual cues and skips redundant analysis steps, rushing through predictable patterns while dedicating more time to ambiguous cases that require deeper examination, thus balancing accuracy with processing efficiency.
Data Source
AI summary
A computer receives a plurality of text and determines a concept is present in the plurality of text. The computer determines a set of hypotheses for the determined concept, wherein the set of hypotheses is a plurality of natural language representations of the determined concept. The computer substitutes the determined concept in the plurality of text with a hypothesis from the determined set of hypotheses. The computer determines the hypothesis is valid based on analyzing the plurality of text with a neural network, wherein the neural network is trained for hypothesis validation. Based on determining that the hypothesis is valid, the computer storing the plurality of text with the determined hypothesis in place of the substituted concept and displays the stored plurality of text.