How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
Causality event extraction demo project including casual patterns and experiment on large scale corpus. 基于因果关系知识库的因果事件图谱实验项目，本项目罗列了因果显式表达的几种模式，基于这种模式和大规模语料，再经过融合等操作，可形成因果事件图谱。
The main features of liuhuanyong/causalityeventextraction are: Knowledge Graphs.
Open-source alternatives to liuhuanyong/causalityeventextraction include: awslabs/dgl-ke — High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings. dstlry/dstlr — scalable knowledge graph construction from unstructured text. facebookresearch/blink — Entity Linker solution. fighting41love/causaldataset. ivendrov/order-embedding — Implementation of caption-image retrieval from the paper "Order-Embeddings of Images and Language". accenture/ampligraph — Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org.
High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.
Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org
