钟晓时 - 北京理工大学

个人简介

钟晓时，博士，北京理工大学计算机学院预聘助理教授，硕士生导师，入选国家级青年人才。本科毕业于北京航空航天大学，计算机科学与技术专业；博士毕业于新加坡南洋理工大学，计算机科学专业自然语言处理和生物信息学方向，导师为Erik Cambria（IEEE Fellow）和Jagath Rajapakse（IEEE Fellow）。攻读博士学位之前曾在香港科技大学和香港城市大学接受运筹学和仿真优化领域顶尖学者Jeff Hong（讲座教授）两年多的学术训练，学术思维和研究品味受Hong教授影响颇深。现在主要研究方向为数据分析、网络科学和自然语言处理。已经在计算机顶级会议ACL和WWW以及一些重要期刊发表多篇论文，并出版Springer英文专著一本。

研究领域

数据分析、网络科学、社交网络、自然语言处理、大模型

近期论文

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Xiaoshi Zhong, Xiang Yu, Erik Cambria, and Jagath C. Rajapakse. Marshall-Olkin Power-Law Distributions in Length-Frequency of Entities. To appear in Knowledge-based Systems, 2023. (SCI, IF: 8.8). Xiaoshi Zhong and Erik Cambria. Time Expression Recognition and Normalization: A Survey. In Artificial Intelligence Review, 56(9): 9115-9140, 2023. (SCI, 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). 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. (SCI, 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. (SCI, 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. (SCI, IF: 5.418) 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. (SCI, 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). 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, full oral rate: 15.6% (117/751).