Zongfeng Yang

Title: Zongfeng Yang: Innovator in Chinese Clinical Text Data Augmentation

Introduction

Zongfeng Yang is a notable inventor based in Hangzhou, China. He has made significant contributions to the field of natural language processing, particularly in the context of Chinese clinical text data. His innovative approach has led to advancements in data augmentation techniques that enhance the quality and usability of clinical data.

Latest Patents

Zongfeng Yang holds a patent titled "Method and apparatus of NER-oriented Chinese clinical text data augmentation." This patent discloses a method and apparatus for augmenting Chinese clinical text data that is oriented towards Named Entity Recognition (NER). The invention involves processing unannotated and annotated data through a series of preprocessing steps. It predicts concealed parts of information based on retained data and introduces an entity word-level discrimination task for pre-training a span-based language model. The patent also details multiple decoding mechanisms used in the fine-tuning stage, allowing for the conversion of linearized data with entity labels into text vectors. This process culminates in enhanced data generation through both forward and reverse decoding techniques.

Career Highlights

Zongfeng Yang is currently associated with Zhejiang Lab, where he continues to push the boundaries of research in clinical data processing. His work is characterized by a strong focus on improving the efficiency and accuracy of data handling in clinical settings.

Collaborations

Zongfeng collaborates with esteemed colleagues, including Jingsong Li and Lixin Shi. Their combined expertise contributes to the innovative projects at Zhejiang Lab, fostering a collaborative environment that enhances research outcomes.

Conclusion

Zongfeng Yang's contributions to the field of clinical text data augmentation exemplify the impact of innovative thinking in technology. His patent and ongoing work at Zhejiang Lab highlight the importance of advancements in natural language processing for the healthcare industry.