郝戍峰

学历：博士

学科：计算机科学与技术

研究领域或方向：多模态、自然语言处理

邮箱：haoshufeng@tyut.edu.cn

职称：副教授

个人简介
主要课程
主要成果

郝戍峰，副教授，硕士生导师，北京理工大学计算机科学与技术博士，中国计算机学会自然语言处理专委委员，人工智能与模式识别专委委员，大数据专委委员，中国中文信息学会专业会员，山西省通信学会理事。
主要从事多模态、自然语言处理的教学与科研工作。先后主持和承担国家级、省部级项目10余项。在国际学术期刊和会议上发表论文30余篇，包括WWW、SIGIR、EMNLP、IJCAI、ESWA、EAAI、KDD等顶会顶刊。授权国家发明专利8项。指导学生参加A1及以上学科类竞赛获得全国一等奖1项、三等奖2项。
物联网技术概论、自然语言处理
科研项目：
1.山西省科技重大专项计划“揭榜挂帅”项目，转辙机动力系统检测平台及预测性维护平台关键技术研究，202401020101003，2025-01至2027-12，青年挂帅人
2.山西省重点研发计划，海量政务大数据治理与共享交换平台研究，202102020101004 , 2022-01至2024-12，课题负责人
3.山西省基础研究计划自然科学研究面上项目，202503021211032，面向交互式对话的动态情绪深度理解与推理机制研究，2025-12至2028-12，主持
4.2024年省筹资助资金回国留学人员科研项目，基于异质句法增强表示的社交媒体文本情感分析方法研究，2024-061，2024-07至2027-07，主持
5.2022年留学人员科技活动项目择优资助项目，面向网络舆情的深度文本反讽识别关键技术研究，20220009，2022-03至2024-03，主持
6.山西省基础研究计划自然科学研究青年项目，面向网络舆情监测的细粒度情感分析关键技术研究，20210302124168，2022-01至2024-12，主持
7.群智化软件开发基本原理与技术体系研究，国家自然科学基金重大项目，61690205，2017-01至2021-12，参与
8.基于文本相似性的参数推荐与错误参数检测方法研究，国家自然科学基金面上项目，61772071，2018-01至2021-12，参与
9.基于主题形式概念分析的文本处理关键技术研究, 国家自然科学基金青年项目，61502033，2016-01至2018-12，参与

代表性论文：
[1] Ping Liu, Hui Song, Shufeng Hao*, Xiaoning Hao, Zexu Zhang, Usman Naseem. HCSL: Rumor Detection by Integrating Intra-Sample Curriculum Learning and Hierarchical Semantic Learning. WWW, 2026.
[2] Wei zhang, Shufeng Hao*, Chongyang Shi*, Usman Naseem, Jinyan Liu, Ziyu Li. ReRule: Temporal Rule-Augmented Language Modeling for Causal Event Chain Completion. WWW, 2026.
[3] An Lao, Wei Ruan, Qi Zhang, Chongyang Shi*, Shufeng Hao*. Dynamic Lifecycle Induced Authenticity Analysis for Multi-modal Fake News Detection. Expert Systems with Applications. 2025.
[4] Mengting Gui+, Shufeng Hao+, Chongyang Shi, Qi Zhang. Structural Patent Classification Using Label Hierarchy Optimization. EMNLP, 2025.
[5] Yinghan Cheng+, Shufeng Hao+, Chongyang Shi. Logical Data Augmentation for Debiased Zero-Shot Stance Detection. International Conference on Neural Information Processing. 2025.
[6] Ping Liu, Yu Gao, Xiangtian Zheng, Hesong Wang, Yimeng Zhao, Xinru Wu, Zehao Lu, Zhichuan Yue, Yuting Xie, Shufeng Hao*. Integrating Attention Mechanism and Boundary Detection for Building Segmentation from Remote Sensing Images. Frontiers in Neurorobotics, 18:1482051. 2025.
[7] Jianing He, Qi Zhang, Duoqian Miao, Yi Kun, Shufeng Hao, Hongyun Zhang, Zhihua Wei. Improving Prediction Certainty Estimation for Reliable Early Exiting via Null Space Projection. IJCAI, 2025.
[8] Dengao Li, Zhichao Gao, Shufeng Hao, Ziyou Xun, Jiajian Song, Jie Cheng, Jumin Zhao. E-Mamba: An efficient Mamba point cloud analysis method with enhanced feature representation. Neurocomputing. 2025.
[9] Kun Yi, Jingru Fei, Qi Zhang, Hui He, Shufeng Hao, Defu Lian, Wei Fan. FilterNet: Harnessing Frequency Filters for Time Series Forecasting. NeurIPS. 2024.
[10] Shufeng Hao, Yu Zhou, Ping Liu, Shuang Xu. Bi-syntax Guided Transformer Network for Aspect Sentiment Triplet Extraction. Neurocomputing 594: 127880, 2024.
[11] Ping Liu, Shuaijie Tian, Yu Gao, Yuting Xie, Shufeng Hao*. EfficientFusion: Simple and Efficient Learning with Pixel-level Fusion for Semantic Segmentation. Multimedia Systems, 30(6): 364. 2024.
[12] Luyao Yu+, Shufeng Hao+, An Lao, Chongyang Shi, Zheng Yang. Related Work Generation with Variational Sequential Planning. ICONIP. 2024.
[13] Shufeng Hao, Jikun Yao, Chongyang Shi, Yu Zhou, Shuang Xu, Dengao Li, Yinghan Cheng: Enhanced Semantic Representation Learning for Sarcasm Detection by Integrating Context-Aware Attention and Fusion Network. Entropy, 25(6): 878, 2023.
[14] Yu Zhou, Haixia Zheng, Xin Huang, Shufeng Hao, Dengao Li, Jumin Zhao. Graph Neural Networks: Taxonomy, Advances, and Trends. ACM Transactions on Intelligent Systems and Technology, 13(1): 15:1-15:54, 2022.
[15]Shufeng Hao, Chongyang Shi, Zhendong Niu, Longbing Cao, Ping Guo. Learning Deep Relevance Couplings for Ad-hoc Document Retrieval. Expert Systems with Applications, 183: 115335, 2021.
[16] Guangyi Hu, Chongyang Shi, Shufeng Hao, Yu Bai. Residual-Duet Network with Tree Dependency Representation for Chinese Question-Answering Sentiment Analysis. SIGIR, 2020.
[17] Shufeng Hao, Chongyang Shi, Zhendong Niu, Longbing Cao. Modeling Positive and Negative Feedback for Improving Document Retrieval. Expert Systems with Applications, 120:253-261, 2019.
[18] Shufeng Hao, Chongyang Shi, Zhendong Niu, Longbing Cao. Concept Coupling Learning for Improving Concept Lattice-based Document Retrieval. Engineering Applications of Artificial Intelligence, 69:65-75, 2018.