Causality Phrase Pair Linking for Social Scenario Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current question-answering systems cannot predict future events considering every risk and chance, limiting their ability to provide information useful for decision-making in complex scenarios like social movements or global influences.
Innovation Solution
A scenario generating apparatus that collects and links causality phrase pairs to generate social scenarios, using join information and polarity assignments to create chains of causality, allowing for the prediction of future events by identifying consistent and relevant relationships between phrases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional question-answering systems are used, then they can provide accurate answers to factual questions, but they cannot predict future events or generate social scenarios
Solution Approach 1:
The system segments future event prediction into discrete causality phrase pairs extracted from documents. Each phrase pair represents a cause-effect relationship that can be independently stored, retrieved, and chained to form scenario sequences, enabling systematic prediction of future events
Solution Approach 2:
The system performs preliminary extraction and storage of causality phrase pairs from documents before prediction is needed. This pre-processing creates a reusable knowledge base of cause-effect relationships that can be quickly assembled into scenario predictions when future events need to be forecasted
2Measurement precision
If a huge amount of documents are analyzed to collect causality information, then the scope and accuracy of prediction improve, but the complexity of information processing increases
Solution Approach 1:
The system extracts only the essential causality phrase pairs from documents, separating the critical cause-effect relationships from the rest of the document content. This extraction focuses processing on the most relevant information while discarding unnecessary details, reducing overall system complexity
Solution Approach 2:
The system transforms unstructured document text into structured causality phrase pairs with defined parameters (cause phrase, result phrase, polarity). This parameterization enables efficient storage, retrieval, and processing of causality information while maintaining high extraction accuracy
3Adaptability or versatility
If causality phrase pairs are extensively linked to form comprehensive scenarios, then the scope of prediction expands, but the computational time and resources increase
Solution Approach 1:
The system creates universal join information that can be applied across multiple different scenario contexts. A single causality phrase pair can be chained with different other phrase pairs to generate various scenario variations, enabling comprehensive scenario coverage without proportionally increasing processing requirements
Solution Approach 2:
The system generates scenarios by chaining phrase pairs until predetermined end conditions are satisfied, rather than exhaustively exploring all possible combinations. This partial action approach produces sufficient scenario coverage for practical decision-making while maintaining reasonable generation speed
Data Source
AI summary
[Object] An object of the present invention is to provide a system for collecting elements as a basis for generating a social scenario useful for people to make well-balanced good decision.[Solution] A scenario generating apparatus includes: a causality phrase pair DB 70 storing causality phrase pairs; synonimity generators 600, 602 and 604, searching, for each of the causality phrase pairs, causality phrase pairs having a cause phrase with causal consistency with the result phrase of the causality phrase pair, and generating join information for joining causality phrase pairs; join relation DB 610 storing the join information; and a causality joining unit 612 joining, to a result phrase of a causality phrase pair, a causality phrase pair having a cause phrase with causal consistency with the phrase by using the join information, and thereby linking causality. A join relation generator 606 that finds a hidden relation between phrases and linking causality phrase pairs may be provided.