Multi-View Entity Relationship Extraction Without External Parsers
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Solution Overview
Problem
Conventional information extraction systems lack accuracy and flexibility due to reliance on external parsers, limiting their effectiveness across different knowledge domains and languages.
Innovation Solution
A multi-view relation extraction system utilizing a ranked neurons long short-term memory neural network and a self-attention neural network to determine structural and semantic importance of words, combined with constraints like Kullback-Leibler divergence and mutual information loss, to ensure consistency and improve accuracy in extracting relationships between entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional information extraction systems use external parsers, then the system structure is simpler, but the accuracy and flexibility across different knowledge domains and languages deteriorates
Solution Approach 1:
The system segments the extraction task into multiple independent neural network components: a structural view network for syntactic analysis, a semantic view network for meaning representation, and a relation extraction network. Each component specializes in one aspect, improving overall accuracy while maintaining modular simplicity.
Solution Approach 2:
The system adds a new dimension by introducing dual-view processing (structural and semantic perspectives) instead of relying on a single external parser. This multi-dimensional approach captures both syntactic and semantic information, significantly improving extraction accuracy across different domains and languages.
2Device complexity
If conventional information extraction systems use external parsers, then the device complexity is reduced, but the adaptability across different knowledge domains and languages deteriorates
Solution Approach 1:
The neural network-based system provides universal adaptability by learning domain-agnostic linguistic patterns and semantic relationships. The model can be applied across different knowledge domains and languages without requiring domain-specific external parsers, achieving multi-functionality in a single unified architecture.
Solution Approach 2:
The system employs dynamic attention mechanisms that adaptively weight different structural and semantic features based on the input context. This dynamic processing allows the system to flexibly adjust to different domains and languages, capturing relevant patterns without being constrained by fixed external parser rules.
3Measurement precision
If the system uses multiple neural networks for structural and semantic analysis, then the extraction accuracy improves, but the device complexity increases
Solution Approach 1:
The system merges the structural and semantic analysis into a unified multi-view framework where both views process the same input sequence simultaneously. The results from both networks are combined through a fusion mechanism, achieving high accuracy while avoiding the redundancy of completely separate processing pipelines.
Solution Approach 2:
The system introduces an intermediary relation extraction network that receives inputs from both structural and semantic view networks. This intermediary component integrates the outputs from multiple networks, coordinating their results to produce the final extraction while managing the complexity of having multiple neural networks.
4Stability of the object's composition
If the system implements multiple constraints (Kullback-Leibler divergence, mutual information loss) to ensure consistency, then the structural-semantic consistency improves, but the computational complexity increases
Solution Approach 1:
The system implements feedback mechanisms through constraint-based loss functions (Kullback-Leibler divergence and mutual information loss) that continuously monitor and adjust the consistency between structural and semantic views during training. This feedback ensures that both views converge toward consistent representations, improving structural-semantic alignment.
Data Source
AI summary
This disclosure describes methods, non-transitory computer readable storage media, and systems that utilize a plurality of neural networks to determine structural and semantic information via different views of a word sequence and then utilize this information to extract a relationship between word sequence entities. For example, the disclosed systems generate a plurality of sets of encoded word representation vectors utilizing the plurality of neural networks. The disclosed system then extracts the relationship from an overall word representation vector generated based on the sets of encoded word representation vectors. Furthermore, the disclosed system enforces structural and semantic consistency between views via a plurality of constrains involving a control mechanism for the semantic view and a plurality of losses.