In the field of natural language processing (NLP), text classification, entity recognition, relationship recognition, and triplet recognition are important topics. This article will delve into these key issues and introduce related algorithms and techniques.
Text Categorization
First, we focus on text classification. Different text classification algorithms and techniques are introduced in detail, covering methods based on machine learning and deep learning.
nlp series (1) text classification (TextCNN) pytorch_textcnn model_Makizichuan's blog-CSDN blog
nlp series (3) text classification (Bert+TextCNN) pytorch_bert textcnn_Makiko Chuan's blog-CSDN blog
Entity recognition
Entity recognition is to identify and extract entities with specific meanings from text, such as names of people, places, organizations, etc. This task is critical for building knowledge graphs, information extraction, and question answering systems. By accurately identifying and labeling entities, we can better understand the information in the text.
Relationship recognition
Relation recognition is to identify and understand the relationships between entities in text. It helps build semantic relationship networks and knowledge graphs, and provides a deeper understanding of the connections between entities.
nlp series (7) Relationship recognition (Bert) pytorch_Makzichuan's blog-CSDN blog
Triplet recognition
Triplet recognition represents these relationships in a structured form, such as subject-predicate-object format. Such a representation makes further reasoning and question answering more convenient and accurate. These technologies are important for building knowledge graphs as well as reasoning and question answering systems.
nlp series (8) Triplet recognition (Bert+CRF) pytorch_Makzichuan's blog-CSDN blog
Overall, this article provides a comprehensive overview covering core issues in NLP such as text classification, entity recognition, relationship recognition, and triplet recognition. By gaining an in-depth understanding of these topics, we can better understand and apply related technologies in the field of natural language processing, laying the foundation for future research and applications.
GitHub Pytorch-NLP 
https://github.com/mzc421/pytorch-nlp/tree/master The complete directory is as follows: There is detailed code analysis in the code
Rigid standards cannot actually limit our infinite possibilities, so! Come on, young men!