Causal Classifier Neural Network for NLP Intent Analysis
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Solution Overview
Problem
Conventional intelligent systems relying on Natural Language Processing (NLP) and Natural Language Understanding (NLU) primarily focus on identifying user intent, which is insufficient for understanding user queries fully, as they do not account for the underlying cause or reason behind the intent, known as the causal, which is crucial for providing comprehensive resolutions.
Innovation Solution
A method and device that utilize a computing device to determine parameters for each target word in a sentence, process these parameters through a causal classifier neural network to identify causal tags, and extract the causal text, enabling the system to provide a more relevant response based on the underlying reason for the user's query.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional NLP/NLU systems focus only on identifying user intent, then the system operation is simple and fast, but the understanding of user queries is incomplete without the underlying cause
Solution Approach 1:
The system segments the user query into distinct components: intent identification and causal extraction. By dividing the processing task into separate modules (intent classifier and causal classifier), the system can extract causal information without requiring complete redesign of the entire NLP pipeline, thus reducing the complexity burden while improving information completeness.
Solution Approach 2:
The patent introduces an intermediary causal classifier module that sits between the intent identification stage and the response generation stage. This intermediary component extracts causal information as a separate layer, allowing the system to maintain its原有 intent-based architecture while adding causal understanding capability without fully redesigning the system.
2Measurement precision
If the system extracts causal information using neural network classifiers, then the understanding precision is improved, but the computational time and processing complexity increase
Solution Approach 1:
The system applies partial action by focusing the neural network classifier only on identifying causal tags for specific target words rather than analyzing the entire sentence structure. This selective approach maintains high precision in causal identification while reducing the overall computational burden and processing time compared to comprehensive sentence analysis.
Solution Approach 2:
The patent transforms the causal extraction problem into a parameter classification problem by defining specific causal tags (B-CAU, I-CAU, O) for target words. By changing the approach from general semantic analysis to specific parameter tagging, the system achieves precise causal identification through efficient neural network classification with reduced computational complexity.
Data Source
AI summary
A method and device for extracting causal from natural language sentences is disclosed. The method includes determining, by a computing device, a plurality of parameters for each target word in a sentence inputted by a user. The method further includes processing for each target word, by the computing device, an input vector comprising the plurality of parameters for a causal classifier neural network. The method includes identifying, by the computing device, causal tags associated with each target word in the sentence based on processing of associated input vector. The method includes extracting, by the computing device, the causal text from the sentence based on the causal tags associated with each target word in the sentence. The method further includes providing, by the computing device, a response to the sentence inputted by the user based on the causal text extracted for the sentence.