Semantic Graph Traversal for Natural Language Intent Recognition
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Solution Overview
Problem
Existing natural language processing systems face challenges in understanding and interpreting multiple intents within natural language inputs due to their reliance on specifically trained classifiers, which require large amounts of labeled data and are limited to specific types of inputs, making them computationally expensive and inflexible.
Innovation Solution
The method involves constructing a semantic graph from the input data, where nodes represent spans of words with shared semantic roles and edges represent semantic relationships, allowing for the formation of inferred clauses by selecting combinations of nodes that form contiguous series, thereby determining multiple intents without the need for specifically trained classifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specifically trained machine learning classifiers are used to determine multiple intents, then intent recognition accuracy can be improved, but computational cost and memory requirements increase significantly
Solution Approach 1:
The patent extracts and removes the need for specifically trained machine learning classifiers by using semantic graph traversal. Instead of relying on complex trained models, the system extracts meaning directly from semantic graphs constructed from natural language inputs, thereby reducing computational overhead while maintaining intent recognition capability
Solution Approach 2:
The patent uses semantic graphs as a simplified representation copy of natural language meaning, avoiding the need for complex trained classifiers. The semantic graph captures the essential semantic relationships needed for intent recognition in a more computationally efficient format
2Measurement precision
If specifically trained classifiers are used for intent determination, then accuracy for specific input types can be improved, but adaptability to wider varieties of inputs deteriorates
Solution Approach 1:
The patent implements a universal semantic graph traversal approach that can handle multiple types of natural language inputs without requiring separate trained classifiers for each input type. The semantic graph structure and traversal methodology provide a multi-functional framework that adapts to various input formats and intent types
Solution Approach 2:
Instead of training classifiers to adapt to different input types, the patent inverts the approach by using a single semantic graph traversal method that naturally adapts to different input types. The system doesn't force inputs to fit pre-trained models but rather allows the semantic graph construction and traversal to handle diverse inputs uniformly
3Reliability
If semantic graph traversal with all possible descendent combinations is performed, then complete intent coverage is achieved, but computational complexity increases
Solution Approach 1:
The patent segments the semantic graph traversal into manageable components by organizing the process around root nodes and their descendants. The method divides the complex parsing task into systematic steps: identifying root nodes, finding descendants, forming combinations, and constructing clauses, thereby reducing overall complexity while maintaining completeness
Solution Approach 2:
The patent performs preliminary organization of semantic graph structures before traversal by establishing clear hierarchies with root nodes and descendants. This preliminary structuring enables more efficient traversal and combination formation, reducing the complexity of the subsequent intent determination process
Data Source
AI summary
Embodiments described herein provide a more flexible, effective, and computationally efficient means for determining multiple intents within a natural language input. Some methods rely on specifically trained machine learning classifiers to determine multiple intents within a natural language input. These classifiers require a large amount of labelled training data in order to work effectively, and are generally only applicable to determining specific types of intents (e.g., a specifically selected set of potential inputs). In contrast, the embodiments described herein avoid the use of specifically trained classifiers by determining inferred clauses from a semantic graph of the input. This allows the methods described herein to function more efficiently and over a wider variety of potential inputs.