Deep Learning Entity Relation Extraction for Unstructured Text
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Solution Overview
Problem
Current methods for extracting entity relations from massive unstructured information are inadequate in comprehensiveness and accuracy, limiting effective information search, knowledge mining, and scientific hypothesis generation.
Innovation Solution
A deep learning-based method and device for extracting entity relations, involving preprocessing of text to identify entities and candidate relations, using a deep learning model to determine entity pairs and their relations, and calculating confidence levels for relation determination, allowing for comprehensive and accurate extraction of entity relations without field limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If schema-based relation mining is used, then relation extraction is structured and organized, but the extraction is limited to predefined relations and reduces comprehensiveness
Solution Approach 1:
The patent employs a unified neural network model that can handle multiple relation types simultaneously without requiring separate models for each relation. The model processes entity pairs and determines various relation types (e.g., temporal, spatial, semantic relations) through a single flexible architecture, enabling the system to adapt to different relation categories while maintaining structured extraction capabilities
Solution Approach 2:
The system dynamically adjusts extraction parameters based on the input text characteristics. The neural network model modifies its processing approach according to the specific entities and context encountered, allowing it to transition between different relation extraction modes and handle both predefined and emergent relation types effectively
2Adaptability or versatility
If subject-object mining is used, then the mining process is flexible and adaptive, but the accuracy and comprehensiveness of entity relation extraction deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the neural network model processes entity pairs and generates relation predictions, which are then refined through confidence level calculations. The system uses the output of one processing stage to inform and improve subsequent extraction decisions, ensuring both flexibility in handling diverse entity pairs and precision in determining their relations
Solution Approach 2:
The system replaces traditional rule-based or manual entity relation extraction mechanisms with a neural network-based intelligent system. This substitution enables the model to automatically learn and infer relations from data, providing both the flexibility to handle various entity types and the precision needed for accurate relation determination through deep learning patterns
3Measurement precision
If deep learning model is applied, then extraction comprehensiveness and accuracy are improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the entity relation extraction process into distinct modular stages: entity pair identification, candidate relation generation, neural network-based relation determination, and confidence level calculation. Each module performs a specific function, reducing the overall computational complexity by breaking down the complex deep learning task into manageable, specialized components that can be processed efficiently
Data Source
AI summary
A method and device for extracting entity relation based on deep learning and a server are provided. The method includes: preprocessing a text to be mined, to obtain a sentence with entities in the text to be mined; determining an entity pair in the sentence according to the entities, wherein the entity pair includes at least two entities, and determining candidate relations between entities in the entity pair; and determining an entity relation between the entities in the entity pair from the candidate relations.