1. stock-knowledge-graph
A small knowledge graph (knowledge base) construction using data published on the web.
利用网络上公开的数据构建一个小型的证券知识图谱（知识库）。
2. open-entity-relation-extraction
Knowledge triples extraction (entities and relations extraction) and knowledge base construction based on dependency syntax for open domain text.
基于依存句法分析，实现面向开放域文本的知识三元组抽取（实体和关系抽取）及知识库构建。
参考：
Chinese Open Relation Extraction and Knowledge Base Establishment (TALLIP 2018), Jia S et al. [paper]
3. RE-CNN-pytorch
Pytorch Implementation of Deep Learning Approach for Relation Extraction Challenge(SemEval-2010 Task #8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals) via Convolutional Neural Network with multi-size convolution kernels.
通过多尺寸卷积核卷积神经网络的深度学习方法进行关系抽取/分类的PyTorch实现。
参考：
Relation Classification via Convolutional Deep Neural Network (COLING 2014), D Zeng et al. [paper]
Relation Extraction: Perspective from Convolutional Neural Networks (NAACL 2015), TH Nguyen et al. [paper]
4. BERT-NER-pytorch
PyTorch solution of Chinese Named Entity Recognition task with Google AI's BERT model.
利用Google AI的BERT模型进行中文命名实体识别任务的PyTorch实现。
参考：
BERT: Pre-training of Deep Bidirectional Trasnsformers for Language Understanding (2018), Devlin et al. [paper]
5. technical-books
常用的技术书籍，内容主要涉及自然语言处理，机器学习，深度学习，算法，编程及数学等。
Keywords: 知识图谱 知识库构建 知识三元组抽取 实体识别 关系抽取 关系分类 开源代码 GitHub
本文项目代码将持续维护与更新。
Welcome to watch, star or fork my project.
