Full-time Deputy Director of the Institute of Cyber Security at Hunan University of Technology and Business, China📖
Dr. Xingliang Mao is an Associate Professor at the Institute of Big Data and Internet Innovation, Hunan University of Technology and Business, China. With a robust academic background, he earned his Ph.D. in Information Systems and Management from the National University of Defense Technology in Changsha, China, in 2018. Dr. Mao specializes in Natural Language Processing (NLP) and Text Mining, and his research contributions have advanced the understanding and applications of these technologies in various domains.
Profile
Scopus Profile
Education Background🎓
- Ph.D. in Information Systems and Management
National University of Defense Technology, Changsha, China (2018)
- Master’s Degree: Relevant details available upon request.
Professional Experience🌱
Dr. Mao currently serves as an Associate Professor at the Institute of Big Data and Internet Innovation at Hunan University of Technology and Business. Prior to this role, he contributed significantly to research and development in academia, where he focused on applying advanced NLP and Text Mining techniques. Dr. Mao has also been actively involved in collaborative research, guiding students and contributing to the academic community through his expertise in data science and machine learning.
- Natural Language Processing (NLP)
- Text Mining
- Data Science and Machine Learning
- Information Retrieval and Knowledge Discovery
Dr. Mao’s research aims to explore the capabilities of NLP in automating complex text-based tasks, developing new algorithms, and improving machine understanding of human language.
Author Metrics
Publications Top Notes 📄
1. Multi-label Text Classification with Enhancing Multi-granularity Information Relations
- Authors: Li, F.-F., Su, P.-Z., Duan, J.-W., Zhang, S.-C., Mao, X.-L.
- Journal: Ruan Jian Xue Bao / Journal of Software, 2023, 34(12), pp. 5686–5703
- Citations: 1
- Abstract: This research focuses on improving multi-label text classification by enhancing the multi-granularity information relations within the text. The proposed approach is shown to boost performance on datasets with complex multi-label characteristics.
2. Generative Named Entity Recognition Framework for Chinese Legal Domain
- Authors: Mao, X., Jiang, J., Zeng, Y., Zhang, S., Li, F.
- Journal: PeerJ Computer Science, 2024, 10, e2428
- Citations: 0
- Abstract: This paper introduces a generative framework for Named Entity Recognition (NER) in the Chinese legal domain. The framework utilizes deep learning to improve entity recognition accuracy in legal documents, which is crucial for automation and legal data analysis.
3. Multi-task Joint Training Model for Machine Reading Comprehension
- Authors: Li, F., Shan, Y., Mao, X., Liu, X., Zhang, S.
- Journal: Neurocomputing, 2022, 488, pp. 66–77
- Citations: 9
- Abstract: The paper proposes a multi-task joint training model that addresses challenges in machine reading comprehension. The model simultaneously learns from multiple tasks, improving the accuracy and efficiency of comprehension tasks across a variety of domains.
4. Multi-task Deep Learning Model Based on Hierarchical Relations of Address Elements for Semantic Address Matching
- Authors: Li, F., Lu, Y., Mao, X., Duan, J., Liu, X.
- Journal: Neural Computing and Applications, 2022, 34(11), pp. 8919–8931
- Citations: 10
- Abstract: This paper proposes a multi-task deep learning model that enhances semantic address matching. It utilizes hierarchical relations of address elements, which improves the accuracy of matching addresses in datasets with varying formats and structures.
5. An Intelligent Charging Scheme Maximizing the Utility for Rechargeable Network in Smart City
- Authors: Ren, Y., Liu, A., Mao, X., Li, F.
- Journal: Pervasive and Mobile Computing, 2021, 77, 101457
- Citations: 7
- Abstract: The paper presents an intelligent charging scheme designed to maximize the utility of rechargeable networks within a smart city infrastructure. It ensures optimal energy usage by managing the charging and discharging cycles of the network efficiently.
Conclusion
Assoc. Prof. Dr. Xingliang Mao is a highly deserving candidate for the Best Researcher Award due to his exceptional academic background, innovative research in NLP and Text Mining, and significant contributions to both the academic and professional communities. His work has the potential to advance many sectors, including legal, smart city infrastructure, and cybersecurity. With improvements in the citation impact and broader industry collaboration, Dr. Mao’s research will continue to set benchmarks for excellence in these critical fields. His leadership in academia and research, coupled with his ongoing contributions, solidifies his position as a leading researcher in his field