Annotated Semantic Graph for Latent Ambiguity Resolution
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Solution Overview
Problem
Natural language processing systems face challenges in handling latent ambiguity, where words appear clear but can apply to multiple senses, leading to unclear meanings, especially in contexts requiring specific interpretations.
Innovation Solution
A computer-implemented method using an annotated semantic graph with nodes representing semantic concepts, where latent ambiguity is marked and processed using a pre-defined method, involving statistical models to predict ambiguity and apply appropriate processing techniques such as ignoring, boosting, or penalizing nodes, and replacing ambiguous nodes with more specific alternatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional word-sense disambiguation is used to resolve obvious ambiguity, then clear word meanings are achieved, but latent ambiguity remains unresolved leading to imprecise interpretations
Solution Approach 1:
The patent applies preliminary action by pre-annotating the semantic graph with latent ambiguity information before NLP processing occurs. Nodes in the semantic graph are marked with latent ambiguity annotations during graph construction, so that when natural language input is processed, the system already has pre-prepared disambiguation strategies ready to apply, rather than attempting to resolve latent ambiguity during the processing stage
2Adaptability or versatility
If semantic graphs are used to represent knowledge, then semantic relationships are captured, but latent ambiguity in nodes is not identified or handled
Solution Approach 1:
The patent applies local quality by making specific nodes in the semantic graph have different properties based on their latent ambiguity characteristics. Rather than treating all nodes uniformly, the system annotates individual nodes with latent ambiguity information, allowing the semantic graph to maintain its versatile semantic representation capability while simultaneously enabling targeted detection and handling of latent ambiguity at the node level
3Measurement precision
If context-dependent meanings are required for accurate interpretation, then processing complexity increases, but without context dependency, accuracy decreases
Solution Approach 1:
The patent applies the intermediary principle by introducing specially processed nodes in the semantic graph that act as mediators between the natural language input and the final interpretation. These annotated nodes serve as intermediaries that carry latent ambiguity information through the processing pipeline, enabling context-dependent resolution without requiring the entire system to become more complex. The annotated nodes mediate the disambiguation process by providing pre-computed contextual clues
Data Source
AI summary
Mechanisms are provided for latent ambiguity handling in natural language processing. The method may include: providing an annotated semantic graph based on a knowledge base in which nodes representing semantic concepts are linked by semantic relationships, wherein one or more nodes are annotated to indicate a latent ambiguity of the semantic concept; processing a natural language input by activation of the annotated semantic graph; during processing when encountering an annotated node indicating a latent ambiguity of the semantic concept, applying a pre-defined latent ambiguity process to the node; and replacing any annotated nodes indicating a latent ambiguity of the concept remaining in an output of the processing of the natural language input with an alternative node from the semantic graph.