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A concept and obvious expression pattern collection of Chinese compound event extraction which then be evolved into ComplexEventGraph，本项目提出了中文复合事件的概念与显式模式，包括条件事件、因果事件、顺承事件、反转事件等事件抽取，并形成事理图谱。
The main features of liuhuanyong/complexeventextraction are: Event Extraction, Information Extraction.
Projects with overlapping indexed features include: varunshenoy/graphgpt — GraphGPT is an LLM knowledge graph generator that extracts entities and relationships from unstructured text to create… thunlp/opennre — OpenNRE is a natural language processing library and neural relation extraction framework designed to transform… harderthenharder/transformers_tasks — Transformers Tasks is a collection of toolkits and scripts dedicated to language model fine-tuning, natural language… yuanxiaosc/entity-relation-extraction — Entity-Relation-Extraction is a machine learning framework designed to identify entities and their logical connections… crownpku/information-extraction-chinese — Chinese Named Entity Recognition with IDCNN/biLSTM+CRF, and Relation Extraction with biGRU+2ATT 中文实体识别与关系提取. baptisteblouin/eventextractionpapers — A list of NLP resources focused on event extraction task.
OpenNRE is a natural language processing library and neural relation extraction framework designed to transform unstructured text into structured relational data. It serves as a toolkit for identifying relationship types between entities and generating entity-relation-entity triples to populate and expand knowledge bases. The framework provides tools for both supervised and distantly supervised relation extraction, allowing neural models to be trained on labeled datasets or via automated pipelines that align knowledge base triples with raw text. The project covers a full information extracti
GraphGPT is an LLM knowledge graph generator that extracts entities and relationships from unstructured text to create visual knowledge graphs. It functions as a natural language graph interface and an unstructured data extraction pipeline, transforming raw text into structured triples for mapping complex information networks. The system enables dynamic knowledge mapping by allowing users to build and update network visualizations through conversational queries and text-based instructions. This allows for the conversion of unstructured data into visual graphs to identify patterns and connecti
Transformers Tasks is a collection of toolkits and scripts dedicated to language model fine-tuning, natural language processing tasks, and transformer-based pipelines. The project functions as a natural language processing toolkit and transformer pipeline library, providing Python scripts and algorithms designed to adapt foundational language models and route text inputs through modular processing workflows. The repository covers supervised fine-tuning pipelines and reinforcement learning alignment procedures that optimize generative text outputs through reward modeling and policy gradient lo
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 bou