Hui Liu (刘慧) | Shanghai University of International Business and Economics

About Me

I am Hui Liu (刘慧), an Associate Professor at the School of Statistics and Data Science(SDS, 统计与数据科学学院), Shanghai University of International Business and Economics (SUIBE, 上海对外经贸大学). In 2009, I earned my Ph.D. in Engineering with a specialization in Computer Science (Computational Linguistics) from Shanghai Jiao Tong University (上海交通大学). After graduating in 2009, I joined the predecessor of SUIBE, which was formerly known as Shanghai Institute of Foreign Trade.

My academic pursuits are centered around Natural Language Processing, with a particular focus on semantic computing and knowledge graphs. Over the years, I have authored over 60 papers in esteemed journals and conferences, including IEEE Trans. Cybern. (formerly TSMCB), SIGHAN, IJCAI, ECAI and WSDM, etc. Moreover, I have completed a MOE (Ministry of Education of China) Project of Humanities and Social Sciences.

As an educator at SUIBE, I have been teaching various courses such as C programming, Introduction to Artificial Intelligence, and Text Mining. My dedication to teaching excellence has been recognized with accolades such as the First Prize for Excellent Teaching Achievements in Shanghai (上海市教学成果奖一等奖) and the Grand Prize for Teaching Achievements at SUIBE. From 2012 to 2023, I had the privilege of serving as the Deputy Dean at my current school. I played a important role in establishing the school’s first Master’s programs, including the program of Mathematical Economics, the program of Management in Business Information Management and the program of Applied Statistics.

Here you can find my profile in Chinese on the college’s official website.

Educations

2009: Ph.D. in Engineering, Department of Computer Science and Engineering, Shanghai Jiao Tong University
2003: Bachelor in Engineering, Department of Computer Science and Engineering, Shanghai Jiao Tong University

Selected Publications

Nijia Mo, Bo Ren, Yongda Wei, and Hui Liu* (2026). HiTower: Hierarchical Interest Modeling with Adaptive Gating for Personalized Recommendation. In IJCNN 2026 (Accepted)
Nijia Mo, Jianxiang Zang, Zhan Wang, and Hui Liu* (2025). DDualSE: Decoupled Dual-head Squeeze and Excitation Attention for Sequential Recommendation. In Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining (WSDM ‘25). Association for Computing Machinery, New York, NY, USA, pp. 300–308. https://doi.org/10.1145/3701551.3703509
Yifang Qiang, Gaojie Sun and Hui Liu* (2024). PatentALL: Multi-label Patent Classification using Adaptive Label Learning. In Proceedings of 2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI), Herndon, VA, USA, pp. 108-115.
Chengcheng Hao, Hui Liu*, Wenping Shi, Shaoyun Zhang (2024). Biterm Tensor Topic Model for Short Reviews in Recommender System. In Proceedings of 2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI), Herndon, VA, USA, pp.152-158.
Zang, J., & Liu, H*. (2024). Modeling Selective Feature Attention for Lightweight Text Matching. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, ĲCAI-24 (pp. 6624–6632). International Joint Conferences on Artificial Intelligence Organization.
Zang, J., & Liu, H*. (2024). Explanation Based Bias Decoupling Regularization for Natural Language Inference. In IJCNN 2024.
Zang, J., & Liu, H. (2023). Improving Text Semantic Similarity Modeling Through a 3D Siamese Network. In ECAI 2023 (pp. 2970-2977). IOS Press.
Zang, J., & Liu, H. (2023). How to Extract and Interact? Nested Siamese Text Matching with Interaction and Extraction. In International Conference on Artificial Neural Networks (pp. 523-535). Cham: Springer Nature Switzerland.
Liu, L., Wu, X., Liu, H., Cao, X., Wang, H., Zhou, H., & Xie, Q. (2020). A semi-supervised approach for extracting TCM clinical terms based on feature words. BMC Medical Informatics and Decision Making, 20(3), 1-7.
Hui Liu and Jianyong Duan(2017). Geometric Analysis of Concept Vectors based on Similarity Values. Lingua Sinica, December 2017, 3:12，https://doi.org/10.1186/s40655-017-0029-0
Hui Liu and Jianyong Duan (2016). An Analysis of the Relation between Similarity Positions and Attributes of Concepts by Distance Geometry, in the Proceedings of the 17th Chinese Lexical Semantics Workshop (CLSW2016), Singapore, 432-441 (Best Paper Award)
Hui Liu (2016). An Analysis of the Relatedness between Similarity Models for Words, ICIC Express Letters, 10(5), 1071-1078.
Hui Liu and Jianyong Duan (2015). Attribute Construction for Online Products by Similarity Computing. ICIC Express Letters, 9(1):99-106, 2015
Hui Liu and Yuquan Chen (2011). Semantic similarity between complex named entities: An approach using multiple web resources. ICIC Express Letters, 5(1):71 – 76, 2011.
Wu, W. L., Lu, R. Z., Duan, J. Y., Liu, H., Gao, F., & Chen, Y. Q. (2010). Spoken language understanding using weakly supervised learning. Computer speech & language, 24(2), 358-382.
Hui Liu, Jinglei Zhao and Ruzhan Lu (2009), Toward the formal verification of a unification system, IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 39(4), 2009.
钟茂生, 刘慧, & 刘磊. (2009). 词汇间语义相关关系量化计算方法. 中文信息学报, 23(2), 115-122.
Hui Liu and Ruzhan Lu (2008). Word Similarities based on an Ensemble Model Using Ranking SVMs, in Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2008), Vol. 3 (Workshops) , Sydney, Australia, 2008
Zhao, J., Liu, H., & Lu, R. (2007). Semantic labeling of compound nominalization in Chinese. In Proceedings of the Workshop on A Broader Perspective on Multiword Expressions (pp. 73-80).
Liu, H., Zhao, J., & Lu, R. (2008). Computing semantic similarities based on machine-readable dictionaries. In IEEE International Workshop on Semantic Computing and Systems (pp. 8-14). IEEE.
吴尉林, 陆汝占, 段建勇, 刘慧, 高峰, & 陈玉泉. (2008). 基于两阶段分类的口语理解方法. 计算机研究与发展, 45(5), 861-868.
Jiang, F., Liu, H., Chen, Y., & Lu, R. (2004). An enhanced model for Chinese word segmentation and part-of-speech tagging. In Proceedings of the Third SIGHAN Workshop on Chinese Language Processing (pp. 28-32).
Huang, L., Peng, Y., Wu, Z., Yuan, Z., Wang, H., & Liu, H. (2003). Pseudo Context-Sensitive Models for Parsing Isolating Languages: Classical Chinese—A Case Study. In Computational Linguistics and Intelligent Text Processing: 4th International Conference, CICLing 2003 Mexico City, Mexico, February 16–22, 2003 Proceedings 4 (pp. 48-51). Springer Berlin Heidelberg.

Projects

MOE (Ministry of Education of China) Youth Project for Humanities and Social Sciences, “Research on the Construction of Attribute Structure for Newly Emerging Named Entities on the Web,” 2013-2016, Principal Investigator.

Current Courses

Introduction to C Programming
Introduction to Programming (Python)
Introduction to Computer Science
Introduction to Artificial Inteligence
Operating Systems
Text Mining (Graduate Course)

Institutional Service

Vice Chair, the Teaching Steering Committee of School of Statistics and Data Science，2019-
Member, the Teaching Steering Committee of the Master of Statistics Program of SUIBE, 2019-
Member, the Academic Degree Evaluation Subcommittee of School of Statistics and Data Science, 2019-
Member, the Teaching Steering Committee of Shanghai University of International Business and Economics, 2019-2025
Member, the Faculty Development Committee of Shanghai University of International Business and Economics，2019-2025
Deputy Dean, School of Statistics and Information (now School of Statistics and Data Science), 2012-2023

Honors and Awards

2011 - Shanghai Institute of Foreign Trade Young Teacher Teaching Quality Award;
2012- Shanghai Institute of Foreign Trade Bilingual Teacher Teaching Quality Award
2013 - the First Prize for Teaching Achievements in Shanghai for the project “Establishing a Global Operations Center: Practical Exploration in Cultivating Students’ Adaptability to Economic Globalization” was awarded. Listed as the 7th out of 8 contributors in this project.
2020 - the Grand Prize for Teaching Achievements in SUIBE for the project “An Innovative Talent Cultivation Model and Its Practice Oriented towards Enhancing Data Intelligence Processing Capabilities”. Listed as the 2nd out of 8 contributors in this project.
Annual commendation of SUIBE for 2010, 2015, 2018-2020, 2023.