Joint Named-Entity and Relation Extraction for Semantic Triplets
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Solution Overview
Problem
Existing information extraction pipelines for unstructured text suffer from error accumulation and error propagation due to sequential application of named entity recognition (NER) and relation extraction (RE), limiting their applicability to a narrow range of entities and relations.
Innovation Solution
A unified generative framework using a trainable sequence-to-sequence model (BART) performs NER and RE conjunctively, generating semantic triplets from unstructured text through alignment with a knowledge graph, employing distance supervision and a sequence-to-sequence model for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If sequential NER and RE pipelines are used, then the extraction process is simple to implement, but error accumulation and error propagation occur
Solution Approach 1:
The patent combines NER and RE into a single unified neural network model that performs both tasks simultaneously. The model uses shared encoder layers to process input text and generates both entity annotations and relationship triplets through integrated processing, eliminating the sequential pipeline architecture that causes error propagation.
Solution Approach 2:
The unified model serves multiple functions within a single architecture: it performs named entity recognition, relation extraction, and triplet generation simultaneously. The shared encoder and decoder structure allows the model to handle both entity identification and relationship extraction tasks using common computational resources, improving reliability while maintaining ease of implementation.
2Device complexity
If sequential NER and RE pipelines are used, then the system architecture is simple, but the applicability range is narrow
Solution Approach 1:
By merging NER and RE into a single unified model, the system achieves broader applicability while maintaining architectural simplicity. The integrated approach allows the model to learn contextual relationships between entities and their relationships simultaneously, enabling it to handle diverse entity types and relationship patterns that sequential pipelines struggle with.
Solution Approach 2:
The unified neural network model dynamically adjusts its processing based on the input context, allowing it to adapt to different entity types and relationship patterns. The model's attention mechanisms and contextual encoding enable it to flexibly handle varying applicability requirements without increasing architectural complexity.
3Ease of manufacture
If separate NER and RE models are used, then each model can be optimized independently, but error propagation occurs between stages
Solution Approach 1:
The patent merges independent NER and RE optimization into a single unified training process. The model uses shared parameters and joint loss functions to optimize both entity recognition and relationship extraction simultaneously, ensuring that errors do not propagate between separate stages while still allowing independent optimization of each function through the unified architecture.
4Productivity
If traditional information extraction pipelines are used, then processing speed is faster for simple cases, but accuracy decreases due to error accumulation
Solution Approach 1:
The unified model performs preliminary contextual analysis and entity identification in the same processing pass, generating both entity annotations and relationship predictions simultaneously. This preliminary action within a single forward pass maintains processing speed while improving accuracy by preventing error accumulation that would occur in sequential multi-stage pipelines.
Data Source
AI summary
An embodiment includes generating a training dataset by aligning text from a document of a document database with a named entity from a knowledge base. The embodiment generates an enhanced training dataset by updating the training dataset to include a named entity type and a named entity label associated with the named entity. The embodiment trains a natural language processing (NLP) model using the enhanced training dataset resulting in a trained NLP model. The embodiment identifies, using the trained NLP model, the named entity in a block of unstructured text and generates, using the trained NLP model, a target sequence that includes a relationship between the named entity and a tail entity, as well as the named entity type and the named entity label of the named entity and a tail entity type and a tail entity label of the tail entity.