School of Computer Science and Technology
Beijing Institute of Technology, China

• Nanyang Technological University, Singapore
  Doctor of Philosophy, Computer Science
  Supervisors: Prof. Erik Cambria (IEEE Fellow) and Prof. Jagath Rajapakse (IEEE Fellow)
• Beihang University, China
  Bachelor of Engineering, Computer Science and Technology

• Data Science, Data Analytics
• Natural Language Processing, Large Language Models
• Network Science, Scale-Free Networks, Power-Law Distributions

• Xiaoshi Zhong, Muyin Wang*, and Hongkun Zhang*. Is Least-Squares Inaccurate in Fitting Power-Law Distributions? The Criticism is Complete Nonsense. In Proceedings of the ACM Web Conference 2022 (WWW), pages 2748-2758, Virtual Event, Lyon, France, 2022. Research-track paper with oral presentation, acceptance rate: 17.7% (323/1822). [pdf][code]
• Xiaoshi Zhong and Erik Cambria. Time Expression Recognition Using a Constituent-based Tagging Scheme. In Proceedings of the 2018 World Wide Web Conference (WWW), pages 983-992, Lyon, France, 2018. Research-track paper with oral presentation, acceptance rate: 14.7% (170/1155). [pdf][code]
• Xiaoshi Zhong, A.Sun, and Erik Cambria. Time Expression Analysis and Recognition Using Syntactic Token Types and General Heuristic Rules. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), pages 420-429, Vancouver, Canada, 2017. Full paper with oral presentation, and the full oral rate is 15.6% (117/751). [pdf][code][slides][gratitude]

• Xiaoshi Zhong, Chenyu Jin, Mengyu An, and Erik Cambria. XTime: A General Rule-based Method for Time Expression Recognition and Normalization. In Knowledge-Based Systems, 297: 111921, 2024. IF: 8.8. [pdf][code]
• Xiaoshi Zhong*# and Huizhi Liang*. On the Scale-Free Property of Citation Networks: An Empirical Study. In Companion Proceedings of the ACM on Web Conference 2024 (WWW Companion), pages 541-544, Singapore, 2024. Research short paper. [pdf]
• Mengyu An*, Chenyu Jin*, Xiaoshi Zhong#, and Erik Cambria. Time Expression Normalization with Meta Time Information. In Proceedings of the 2023 International Conference on Computational Science and Computational Intelligence (CSCI), pages 695-702, Las Vegas, USA, 2023.
• Xiaoshi Zhong, Xiang Yu, Erik Cambria, Jagath C. Rajapakse. Marshall-Olkin Power-Law Distributions in Length-Frequency of Entities. In Knowledge-Based Systems, 279: 110942, 2023. IF: 8.8. [pdf][code]
• Xiaoshi Zhong and Erik Cambria. Time Expression Recognition and Normalization: A Survey. In Artificial Intelligence Review, 56(9): 9115-9140, 2023. IF: 12.0.
• Xiaoshi Zhong, Muyin Wang*, and Hongkun Zhang*. Is Least-Squares Inaccurate in Fitting Power-Law Distributions? The Criticism is Complete Nonsense. In Proceedings of the ACM Web Conference 2022 (WWW), pages 2748-2758, Virtual Event, Lyon, France, 2022. Research-track paper with oral presentation, acceptance rate: 17.7% (323/1822). [pdf][code]
• Xiaoshi Zhong, Erik Cambria, and Amir Hussain. Does Semantics Aid Syntax? An Empirical Study on Named Entity Recognition and Classification. In Neural Computing and Applications, 34(11): 8373-8384, 2022. IF: 5.606.
• Xiaoshi Zhong and Erik Cambria. Time Expression and Named Entity Recognition. In Book Series Socio-Affective Computing, Volume 10, Springer Nature, 2021. ISBN: 978-3-030-78961-9.
• Xiaoshi Zhong and Jagath C. Rajapakse. Graph Embeddings on Gene Ontology Annotations for Protein-Protein Interaction Prediction. In BMC Bioinformatics, 21(16): 1-17, 2020. IF: 3.242.
• Xiaoshi Zhong, Erik Cambria, and Amir Hussain. Extracting Time Expressions and Named Entities with Constituent-based Tagging Schemes. In Cognitive Computation, 12(4): 844-862, 2020. IF: 5.418.
• Xiaoshi Zhong and Jagath C. Rajapakse. Predicting Missing and Spurious Protein-Protein Interactions Using Graph Embeddings on GO Annotation Graph. In Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 1828-1835, San Diego, CA, USA, 2019.
• Xiaoshi Zhong, Rama Kaalia, and Jagath C. Rajapakse. GO2Vec: Transforming GO Terms and Proteins to Vector Representations via Graph Embeddings. In BMC Genomics, 20(9): 1-10, 2019. IF: 3.730.
• Xiaoshi Zhong and Erik Cambria. Time Expression Recognition Using a Constituent-based Tagging Scheme. In Proceedings of the 2018 World Wide Web Conference (WWW), pages 983-992, Lyon, France, 2018. Research-track paper with oral presentation, acceptance rate: 14.7% (170/1155). [pdf][code]
• Xiaoshi Zhong, A.Sun, and Erik Cambria. Time Expression Analysis and Recognition Using Syntactic Token Types and General Heuristic Rules. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), pages 420-429, Vancouver, Canada, 2017. Full paper with oral presentation, and the full oral rate is 15.6% (117/751). [pdf][code][slides][gratitude]

• None

• Xiaoshi Zhong. A Wikipedia based Hybrid Ranking Method for Taxonomic Relation Extraction. In Proceedings of the 9th Asia Information Retrieval Societies Conference (AIRS), pages 332-343, Singapore, 2013. Full paper with oral presentation, acceptance rate: 24.8% (27/109).
• Xiaoshi Zhong, Yunqing Xia, Zhongda Xie, Sen Na, Qin'an Hu, and Yaohai Huang. Concept-based Medical Document Retrieval: THCIB at CLEF eHealth 2013 Task 3. In Working Notes for CLEF 2013 Conference (CLEF), 2013.
• Yunqing Xia, Xiaoshi Zhong, Peng Liu, Cheng Tan, Sen Na, Qin'an Hu, and Yaohai Huang. Normalization of Abbreviations/Acronyms: THCIB at CLEF eHealth 2013 Task 2. In Working Notes for CLEF 2013 Conference (CLEF), 2013.
• Yunqing Xia, Xiaoshi Zhong, Peng Liu, Cheng Tan, Sen Na, Qin'an Hu, and Yaohai Huang. Combining MetaMap and cTAKES in Disorder Recognition: THCIB at CLEF eHealth 2013 Task 1. In Working Notes for CLEF 2013 Conference (CLEF), 2013.
• Yunqing Xia, Xiaoshi Zhong, Guoyu Tang, Junjun Wang, Qiang Zhou, Thomas Fang Zheng, Qin'an Hu, Sen Na, and Yaohai Huang. Ranking Search Intents Underlying a Query. In Proceedings of the 18th International Conference on Applications of Natural Language to Information Systems (NLDB), pages 266-271, Salford, UK, 2013.

• Scientific training: From October 2013 to January 2016, I worked with Prof. Jeff Hong (Chair Professor) as a research associate at both The Hong Kong University of Science and Technology and the City University of Hong Kong. Before being trained by Prof. Hong, I worked as an engineer. Since that training, I have been thinking like a scientist.
• Chinese calligraphy: One of my childhood hobbies. Here are some samples: 1, 2, 3, 4.