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您现在的位置是: 钟晓时

硕士生导师

姓名:钟晓时
所在学科:计算机科学与技术
职称:助理教授
联系电话:
E-mail:xszhong@bit.edu.cn
通信地址:北京市海淀区中关村南大街5号院中心教学楼

个人信息

钟晓时,博士,正规买球app下载预聘助理教授,特别副研究员,硕士生导师,入选国家级海外引才计划(XX计划)青年项目。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,计算机科学专业计算语言学方向。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世界顶尖学者Jeff Hong(讲座教授)两年多的学术训练,学术思维和研究风格受Hong教授影响颇深。现在主要研究方向为数据分析、复杂系统和计算语言学。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,出版英文专著一本。

招生计划:每年招硕士研究生3~4人,同时欢迎优秀本科生加入研究组。


科研方向

数据分析、复杂系统、计算语言学


代表性学术成果

[1] Xiaoshi Zhong, Muyin Wang, and Hongkun Zhang. Is Least-Squares Inaccurate in Fitting Power-Law Distributions? The Criticism is Complete Nonsense. To appear in Proceedings of the ACM Web Conference 2022 (WWW), Virtual Event, Lyon, France, 2022. Research-track paper with oral presentation, acceptance rate: 17.7% (323/1822).

[2] 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.

[3] Xiaoshi Zhong, Erik Cambria, and Amir Hussain. Does Semantics Aid Syntax? An Empirical Study on Named Entity Recognition and Classification. To appear in Neural Computing and Applications, 2021. (SCI, IF: 5.606)

[4] 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)

[5] 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)

[6] 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)

[7] 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).

[8] 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).


承担科研情况

所获奖励

社会兼职

备注

本页面不常更新,更多信息见个人主页:https://xszhong.github.io


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