Peng Qi

Title: Innovations of Peng Qi in Sentiment Classification

Introduction

Peng Qi is an accomplished inventor based in Mountain View, California. He has made significant contributions to the field of sentiment analysis through his innovative patent. His work focuses on enhancing the accuracy of sentiment classification, which is crucial for various applications in natural language processing.

Latest Patents

Peng Qi holds a patent titled "Method and system for aspect-level sentiment classification by merging graphs." This patent describes a system that includes a computing device equipped with a processor and a storage device. The computer executable code within the device is designed to perform several functions, including receiving an aspect term-sentence pair, embedding the pair, and parsing the sentence using multiple parsers to obtain dependency trees. The system then performs edge union to create a merged graph, combines the embedding with the merged graph to form a relation graph, and applies a relation graph neural network to extract hidden representations of the aspect term. The final step involves classifying the aspect term based on the extracted representation to predict a classification label.

Career Highlights

Peng Qi is currently employed at Chinabank Payment (Beijing) Technology Co., Ltd. His role involves leveraging his expertise in sentiment classification to develop advanced systems that improve the understanding of user sentiments in various contexts. His innovative approach has positioned him as a key player in the field.

Collaborations

Peng Qi has collaborated with notable colleagues, including Xiaochen Hou and Guangtao Wang. Their combined efforts contribute to the advancement of technologies in sentiment analysis and natural language processing.

Conclusion

Peng Qi's contributions to sentiment classification through his innovative patent demonstrate his expertise and commitment to advancing technology in this field. His work continues to influence the development of more accurate sentiment analysis systems.