Hai Zhuge

Hai Zhuge is affiliated with the Chinese Academy of Sciences in China and has contributed extensively to the field of Computer Science, with a notable focus on Artificial Intelligence. Their research spans several subfields, including Computer Vision and Pattern Recognition, Molecular Biology, Information Systems, and Computational Theory and Mathematics.

Their recent publications cover diverse topics such as Topic Modeling, Advanced Text Analysis Techniques, Semantic Web and Ontologies, Natural Language Processing Techniques, Biomedical Text Mining and Ontologies, Multimodal Machine Learning Applications, and Advanced Image and Video Retrieval Techniques.

Hai Zhuge's work has been published in various respected journals and conferences. Frequent publication venues include:

• Expert Systems with Applications
• arXiv (Cornell University)
• Concurrency and Computation Practice and Experience
• Future Generation Computer Systems
• SSRN Electronic Journal

Highlighted recent papers authored or co-authored by Hai Zhuge include:

• Grouping sentences as better language unit for extractive text summarization, 2020, Future Generation Computer Systems
• A news image captioning approach based on multimodal pointer-generator network, 2020, Concurrency and Computation Practice and Experience
• Automatic evaluation of summary on fidelity, conciseness and coherence for text summarization based on semantic link network, 2022, Expert Systems with Applications
• The influence of semantic link network on the ability of question-answering system, 2020, Future Generation Computer Systems
• Automatic construction of classification dimensions by clustering texts based on common words, 2023, Expert Systems with Applications

Their research collaborations involve several frequent co-authors, including:

• Xiaoping Sun
• Sheng Zhuge
• Mengyun Cao
• Jingqiang Chen
• Bing Ma

Hai Zhuge has received professional recognition in the form of ACM Senior Member status awarded in 2009 and ACM Distinguished Member in 2010. The focus of their work reflects an integration of semantic technologies, text analysis, and machine learning techniques applied across various domains.