Adaptive Thresholding for Relation Extraction
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Solution Overview
Problem
Current approaches to document-level relation extraction face challenges with multi-entity and multi-label problems, as they often rely on graph structures or heuristic thresholding, which can introduce noise and require suboptimal threshold tuning, limiting their effectiveness.
Innovation Solution
The introduction of adaptive thresholding and localized context pooling techniques, which replace global thresholds with learnable entity-dependent thresholds and utilize pre-trained language models' attention heads to enhance entity embeddings, respectively, to improve relation extraction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If graph structures are used to bridge entities in document-level relation extraction, then long-distance information capture is improved, but device complexity increases and noise is introduced
Solution Approach 1:
The patent extracts and removes the complex graph structure component from the system, replacing it with direct transformer-based processing. This eliminates the need for constructing document graphs with dependency structures while retaining the ability to capture long-distance relationships through the transformer's inherent attention mechanism.
Solution Approach 2:
The patent substitutes the mechanical graph construction process with a neural network-based attention mechanism. Instead of explicitly building graph structures to model relationships, the transformer model uses self-attention to implicitly capture dependencies between entities regardless of their distance in the document.
2Device complexity
If pre-trained language models are used without graph structures, then device complexity is reduced, but entity representation accuracy deteriorates due to using the same embedding for all entity pairs
Solution Approach 1:
The patent applies local quality by making entity representations context-specific. Instead of using a single global entity embedding, the model generates different entity representations for different entity pairs by applying attention mechanisms localized to each pair's context. This allows each entity to have tailored representations based on its specific relational context.
Solution Approach 2:
The patent segments the entity representation process by creating separate attention-based representations for each entity pair. Rather than pooling all entity embeddings uniformly, the model processes each entity pair independently through attention mechanisms, generating specialized representations for each relationship context.
3Ease of operation
If global threshold is used for multi-label classification, then ease of operation is improved, but measurement precision deteriorates due to heuristic threshold tuning and decision errors
Solution Approach 1:
The patent transforms the static global threshold into a dynamic, learnable parameter. Instead of fixing the threshold value beforehand, the model learns optimal threshold values during training through the adaptive thresholding mechanism. This allows the threshold to adapt to different relationships and contexts, improving classification accuracy while maintaining operational simplicity.
Solution Approach 2:
The patent implements feedback by using the classification results and loss signals to continuously adjust the threshold values during training. The model receives feedback from the ground truth labels and uses this information to refine its threshold parameters, progressively improving its decision-making accuracy for relation extraction.
Data Source
AI summary
System and method for relation extraction using adaptive thresholding and localized context pooling (ATLOP). The system includes a computing device, the computing device has a processer and a storage device storing computer executable code. The computer executable code is configured to provide a document; embed entities in the document into embedding vectors; and predict relations between a pair of entities in the document using their embedding vectors. The relation prediction is performed based on an improved language model. Each relation has an adaptive threshold, and the relation between the pair of entities is determined to exist when a logit of the relation between the pair of entities is greater than a logit function of the corresponding adaptive threshold.