Graph Neural NER for Domain-Specific Entity Relation Modeling
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Solution Overview
Problem
Existing named entity recognition (NER) systems struggle to achieve high performance in specialized domains with complex contexts and diverse entity types, particularly due to limited annotations and challenging domain-specific jargon.
Innovation Solution
The system employs Graph Neural Networks (GNNs) to explicitly connect entity mentions based on global coreference relations and local dependency relations, using Graph Attention Networks (GATs) to enhance entity mention representations, particularly through Entity Relation Graphs (EnRelG).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional NER systems are used in specialized domains, then the system structure remains simple, but the NER performance deteriorates due to limited annotations and domain-specific jargon
Solution Approach 1:
The patent segments the NER system into multiple functional components: embedding layer for word representations, encoding layer for sequential context capture, graph neural network layer for relation-based processing, and decoding layer for entity prediction. This segmentation allows each component to specialize in handling specific aspects of domain-specific NER challenges, improving overall performance while maintaining manageable system complexity
Solution Approach 2:
The patent introduces an entity relation graph as an intermediary structure that mediates between the input text and the NER task. This graph captures coreference and dependency relations among entities, serving as a bridge that enables the system to leverage contextual cues and semantic relations for improved entity recognition in specialized domains
2Measurement precision
If more annotated data is collected for domain-specific NER, then the NER accuracy improves, but the data collection cost and time increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing the input text to identify and extract candidate entities, coreference relations, and dependency relations before the main NER processing. This preliminary extraction of relational information enables the graph neural network to operate more efficiently and accurately, improving NER performance without requiring proportional increases in annotated data
Solution Approach 2:
The patent changes the representation parameters of entities by transforming them from simple word embeddings to enriched embeddings that incorporate graph-based relational information. This parameter transformation allows the system to achieve higher accuracy with limited annotated data by effectively utilizing the structural information from entity relation graphs
Data Source
AI summary
A computer-implemented method of Named Entity Recognition (NER) includes receiving an input, identifying a plurality of candidate entities corresponding to the input, assigning word embeddings to the input at an embedding layer, capturing sequential context of the word embeddings in an encoding layer to obtain encoded word embeddings, constructing an entity relation graph using global coreference relations and local dependency relations to obtain a coreference graph and a dependency graph, fusing the encoded word embeddings, coreference graph, and dependency graph, via a graphical neural network (GNN), to obtain updated word embeddings, and decoding the updated word embeddings via a decoding layer to obtain enriched entity predictions.