yuanxiaosc/Entity-Relation-Extraction
Entity Relation Extraction
Entity-Relation-Extraction is a machine learning framework designed to identify entities and their logical connections within unstructured text. It functions as a pipeline that transforms raw documents into structured knowledge graphs by utilizing deep learning models and transformer architectures.
The project distinguishes itself through a schema-driven approach, which maps extracted information to predefined relational templates to ensure output consistency. It employs a multi-stage process that combines sequence-labeling token classification with contextual encoding to delineate entity boundaries and classify relationships between them.
The toolkit provides components for fine-tuning pre-trained language models and orchestrating data through computational graphs. It includes utilities for evaluating model performance against ground truth datasets to verify the accuracy of the extraction process.
Features
- Entity and Relation Extraction - Identifies specific entities and determines logical connections between them using a multi-stage classification pipeline.
- Natural Language Entity Extraction - Transforms raw documents into structured knowledge graphs by identifying entities and their logical connections.
- Knowledge Graph Construction - Populates structured databases by automatically extracting relationships between entities from large volumes of text.
- Schema-Driven Mappings - Structures extracted data according to predefined relational templates to ensure output conforms to specific domain requirements.
