Information Processing Device for Causal Relationship Generalization
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Solution Overview
Problem
Conventional systems require pre-prepared important word and generalized dictionaries for extracting causal relationships from documents, making it difficult to efficiently convert causal relationships into generalized expressions across different categories.
Innovation Solution
An information processing device that acquires causal relationships from a target document using causal relationship management information and feature management information to generate a generalized expression, eliminating the need for pre-prepared dictionaries by leveraging machine learning and word embedding techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-prepared important word dictionary and generalized dictionary are used to extract causal relationships, then causal relationship extraction can be performed, but the system complexity increases and maintenance becomes necessary
Solution Approach 1:
The system automatically acquires causal relationships from documents and generates generalized expressions without requiring manual dictionary preparation or maintenance. The causal relationship acquisition unit automatically extracts causal relationships from target documents, and the generalized expression generation unit automatically creates generalized expressions based on these relationships, making the system self-sufficient and eliminating the need for external dictionary resources.
2Measurement precision
If category-specific dictionaries are prepared for different document categories, then accurate causal relationship conversion can be achieved, but the time and resources required for dictionary preparation and maintenance increase
Solution Approach 1:
The system uses a universal causal relationship management information structure that can handle multiple document categories without requiring separate dictionaries for each category. The generalized expression generation unit generates category-independent generalized expressions that can be applied across different document types, making the system versatile and eliminating the need for category-specific dictionary preparation.
Solution Approach 2:
The system automatically adapts to different document categories by acquiring causal relationships directly from the target documents themselves, rather than relying on pre-prepared category-specific dictionaries. This self-adaptation capability allows the system to maintain high conversion accuracy across multiple categories without additional time investment for dictionary maintenance.
3Measurement precision
If manual conversion of causal relationships to generalized expressions is performed considering meaning, then conversion accuracy is maintained, but conversion efficiency decreases
Solution Approach 1:
The system replaces manual mechanical conversion processes with automated information processing. The generalized expression generation unit automatically generates generalized expressions by utilizing causal relationship management information and feature management information, substituting human manual work with automated computational processes that maintain accuracy while dramatically improving conversion efficiency.
Data Source
AI summary
An information processing device includes one or more hardware processors. The hardware processors acquire a causal relationship included in a target document that is a specific document from causal relationship management information in which one or a plurality of causal relationships are registered, which are extracted from one or a plurality of documents and each which includes a set of a first element and a second element having a relationship; acquire a similar expression of the causal relationship included in the target document based on feature management information in which features of a plurality of words included in one or a plurality of documents are registered; and acquire a generalized expression of the causal relationship included in the target document based on the causal relationship included in the target document and the similar expression.