Text-Based Factor Causality Mining for Implicit Relationship Detection
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Solution Overview
Problem
Existing technologies fail to uncover the causality between implicit factors contained in unstructured text, leading to incomplete causality mining of related objects.
Innovation Solution
A method and device that determine a group of target factors from unstructured text, identify causal-outcome event pairs, and establish causality between these factors using a self-trained natural language processing model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text analysis methods are used, then the analysis process is simple, but the causality between implicit factors cannot be uncovered
Solution Approach 1:
The system performs preliminary actions by first extracting factors from unstructured text, then identifying event pairs, and finally determining causal relationships between these factors. This multi-stage preliminary processing enables the system to uncover implicit causalities that traditional methods miss, while managing complexity through structured decomposition of the analysis process
Solution Approach 2:
The patent introduces an intermediary layer of event pair extraction between raw text and final causality determination. This intermediary step acts as a bridge that transforms unstructured text into structured event relationships, enabling more accurate causality detection while managing the complexity of analyzing implicit factors in unstructured text
2Loss of information
If unstructured text is analyzed in detail to uncover implicit factors, then causality mining completeness improves, but processing time increases
Solution Approach 1:
The analysis process is segmented into distinct stages: factor extraction from unstructured text, event pair identification, and causality determination. This segmentation allows the system to process information systematically, ensuring comprehensive causality mining while managing processing time through efficient division of analytical tasks
Solution Approach 2:
The system performs preliminary factor extraction and event pair identification before final causality determination. These preliminary actions prepare the data in a structured format that accelerates the final causality analysis, enabling complete information extraction without excessive processing time for the critical causality determination step
Data Source
AI summary
According to the embodiments of the present disclosure, method and device for information processing are provided. This method comprises determining a group of target factors for a target object based on an unstructured text set about the target object. Each target factor represents an aspect of the target object. This method also comprises determining a causal-outcome event pair comprising a causal event and outcome event by analyzing the text in the text set. This method further comprises determining, based on the causal-outcome event pair, a first causality between a first factor in the group of target factors and a second factor of the target object. This scheme helps to improve the mining of causalities among the target object, thereby facilitating the improvement of the target object.