Syntactic Graph Traversal for Natural Language Intent Recognition
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Solution Overview
Problem
Existing natural language processing systems face challenges in accurately determining multiple intents from user inputs due to reliance on specifically trained classifiers that require large amounts of labeled data and are limited to specific types of inputs, leading to computational inefficiencies and reduced flexibility.
Innovation Solution
The method involves determining inferred clauses from a syntactic graph of the input, avoiding the need for specifically trained classifiers by using unsupervised representation learning and established syntactic dependency models, which allows for more flexible and efficient intent recognition across a wider range of inputs.
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 for specific input types, but memory capacity requirements increase and the system becomes limited to specific types of inputs
Solution Approach 1:
The patent applies universality by using a single syntactic graph-based processing system that can handle multiple types of natural language inputs without requiring separate trained classifiers for each input type. The system universally processes intents, entities, and relationships through syntactic analysis rather than relying on input-specific training data, making the system adaptable to diverse input types while maintaining reasonable accuracy.
Solution Approach 2:
The patent uses copying by creating a syntactic graph representation that captures the structural relationships in natural language inputs. Instead of training classifiers on specific input types, the system copies the syntactic structure into a graph format that can be analyzed to extract intents and relationships, allowing the same processing mechanism to handle various input types effectively.
2Measurement precision
If specifically trained machine learning classifiers are used to determine multiple intents, then intent recognition can be performed for specific input types, but computational efficiency decreases due to large amounts of labeled training data requirements
Solution Approach 1:
The patent replaces the mechanical system of training and deploying multiple specialized machine learning classifiers with a syntactic graph-based analysis system. Instead of relying on computationally intensive training processes and large labeled datasets, the system uses syntactic parsing and graph traversal to extract intents and relationships, significantly improving computational efficiency while maintaining intent recognition capability.
Solution Approach 2:
The patent applies self-service by enabling the syntactic graph system to automatically extract intents, entities, and relationships directly from the structure of natural language inputs without requiring external training data or pre-trained models. The system serves itself by using the inherent syntactic information in the input to perform intent recognition, eliminating the need for computationally expensive training processes.
3Loss of information
If semantic graphs are generated from syntactic graphs for intent determination, then semantic meaning can be extracted, but additional computation time and potential errors are introduced
Solution Approach 1:
The patent applies taking out by directly extracting intent information, entities, and relationships from the syntactic graph structure without generating an intermediate semantic graph. The system extracts the necessary semantic meaning directly from syntactic relationships, eliminating the time-consuming semantic graph generation step while still capturing the essential semantic information needed for intent determination.
Solution Approach 2:
The patent inverts the traditional approach by not converting syntactic graphs to semantic graphs, but instead directly analyzing the syntactic graph to extract semantic information. Rather than following the conventional path of syntactic analysis → semantic graph generation → intent extraction, the system takes the reverse approach of syntactic analysis → direct intent extraction, saving computation time and reducing error propagation.
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 typically 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 syntactic graph of the input. This allows the methods described herein to function more efficiently and over a wider variety of potential inputs. Furthermore, by determining inferred clauses from a syntactic graph, rather than a semantic graph, the methods described herein avoid the additional computation and potential additional errors associated with the generation of a semantic graph from a syntactic graph.