yuanxiaosc/Entity-Relation-Extraction
Entity Relation Extraction
Entity-Relation-Extraction 是一个机器学习框架，旨在识别非结构化文本中的实体及其逻辑连接。它作为一个管道，通过利用深度学习模型和 Transformer 架构，将原始文档转换为结构化知识图谱。
该项目通过模式驱动的方法脱颖而出，将提取的信息映射到预定义的关联模板，以确保输出的一致性。它采用多阶段处理流程，结合序列标注令牌分类与上下文编码，以界定实体边界并对它们之间的关系进行分类。
该工具包提供了用于微调预训练语言模型和通过计算图编排数据的组件。它包括用于根据基准数据集评估模型性能的实用程序，以验证提取过程的准确性。
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.
- Transformer Encoders - Processes input text through deep bidirectional layers to generate rich vector representations.
- Information Extraction - Sequences specialized models to identify entities and classify their logical connections within unstructured text.
- BERT Fine-Tuning - Adapts pre-trained language models to perform specialized sequence labeling and classification tasks.
- Graph Schema Extractions - Maps unstructured text into structured knowledge graphs based on predefined entity and relation schemas.
- Machine Learning Pipelines - Builds multi-stage workflows that process raw data through classification and labeling models.
