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 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。
本文项目代码将持续更新。
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