Siamese Neural Network for Relation Extraction
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Solution Overview
Problem
Current machine learning techniques for relation extraction from text require significant human effort and resources, are limited by the need for extensive training data, and struggle with adapting to new knowledge domains, especially when dealing with out-of-vocabulary words and analogies.
Innovation Solution
A hierarchical, multi-layer Siamese neural network is employed to encode natural language strings, using attention mechanisms and bidirectional GRUs to determine the degree of analogical similarity between relationships, allowing for efficient adaptation to new domains with minimal human interaction and leveraging existing knowledge assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning techniques are used for relation extraction, then the system can process structured text data, but it requires significant human effort and extensive training data
Solution Approach 1:
The patent introduces an analogy-based intermediate representation that mediates between training examples and target relations. By computing analogical similarity through vector operations (comparing relationship between word pairs), the system transfers knowledge from seen relations to unseen relations without requiring extensive training data for each specific relation type
Solution Approach 2:
The system copies knowledge from existing training examples by computing analogical similarities. Instead of learning each relation type from scratch, it copies relational patterns from training data and applies them to new contexts through vector-based analogy computation, significantly reducing the training data requirement
2Adaptability or versatility
If traditional machine learning techniques are used for relation extraction, then the system can process known vocabulary, but it struggles with out-of-vocabulary words and adapting to new knowledge domains
Solution Approach 1:
The patent employs a universal vector representation system that can handle any word or relation type. The analogy computation mechanism works uniformly across different domains and vocabulary types, making the system adaptable to new knowledge domains without requiring domain-specific customization or increased complexity
3Reliability
If extensive training data is used with traditional techniques, then the system can achieve better performance on known relations, but it requires significant human effort and resources
Solution Approach 1:
The system performs preliminary vector computations to establish analogical relationships between word pairs. By pre-computing and storing vector representations and analogy scores, the system prepares knowledge in advance that can be quickly applied during relation extraction, improving both reliability and processing efficiency
Data Source
AI summary
A first neural network is operated on a processor and a memory to encode a first natural language string into a first sentence encoding including a set of word encodings. Using a word-based attention mechanism with a context vector, a weight value for a word encoding within the first sentence encoding is adjusted to form an adjusted first sentence encoding. Using a sentence-based attention mechanism, a first relationship encoding corresponding to the adjusted first sentence encoding is determined. An absolute difference between the first relationship encoding and a second relationship encoding is computed. Using a multi-layer perceptron, a degree of analogical similarity between the first relationship encoding and a second relationship encoding is determined.