王津 (Wang, Jin)

教授

H指数:0

被引数:0

个人简介

"Google学术\nhttps://scholar.google.com/citations?hl=zh-CN&user=ZVBB1eAAAAAJ\nDBLP\nhttps://dblp.org/pid/92/1375-8.html\nSemantic Scholar\nhttps://www.semanticscholar.org/author/Jin-Wang/49606589\nGitHub\nhttps://wangjin0818.github.io/\n会议论文\n[1] J. Wang, L. C. Yu, K. R. Lai, and X. Zhang, “Dimensional sentiment analysis using a regional CNN-LSTM model”, in Proceedings of the 54th annual meeting of the association for computational linguistics (ACL), 2016, pp. 225-230.\n[2] J. Wang, L. C. Yu, K. R. Lai, and X. Zhang, “Investigating Dynamic Routing in Tree-Structured LSTM for Sentiment Analysis,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP-2019), 2019, pp. 3423–3428.\n[3] J. Wang, L.C. Yu, and X. Zhang, \"SoftMCL: Soft Momentum Contrastive Learning for Fine-grained Sentiment-aware Pre-training,\" in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), 2024, pp. 15012–15023.\n[4] X. Ma, J. Wang(*), and X. Zhang, “Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation,” in Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence (AAAI-2025), 2025.\n[5] J. Kong, X. Ma, J. Wang(*), and X. Zhang, “Sample-aware Adaptive Structured Pruning for Large Language Models, ” in Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence (AAAI-2025), 2025.\n[6] Y. Zhang, J. Wang(*), L. C. Yu, D. Xu and X. Zhang, “Personalized LoRA for human-centered text understanding, ” in Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence (AAAI-2024), 2024.\n[7] R. Chen, J. Wang(*), L.-C. Yu, and X. Zhang, “Learning to Memorize Entailment and Discourse Relations for Persona-Consistent Dialogues,” in Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-2023), 2023.\n[8] X. Luo, Z. Tang, J. Wang(*), and X. Zhang, “Zero-Shot Cross-Domain Dialogue State Tracking via Dual Low-Rank Adaptation, ” Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL-2024), 2024.\n[9] X. Ma, J. Liu, J. Wang(*), and X. Zhang, “FedID: Federated Interactive Distillation for Large-Scale Pretraining Language Models,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings (EMNLP-2023), 2023, pp. 8566–8577.\n[10] Y. Zhang, J. Wang, L. C. Yu, D. Xu, and Xuejie Zhang, “Multi-Attribute Multi-Grained Adaptation of Pre-Trained Language Models for Text Understanding from Bayesian Perspective”, in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2025), 2025, pp. 25967-25975.\n期刊论文\n[1] J. Wang, L. C. Yu, K. R. Lai, and X. Zhang, “Tree-Structured Regional CNN-LSTM Model for Dimensional Sentiment Analysis,” IEEE/ACM Trans. Audio Speech Lang. Process., vol. 28, pp. 581–591, 2020.\n[2] L. Yuan, J. Wang, L. C. Yu, Y. Cai and X. Zhang, “Pruning Self-Attention with Local and Syntactic Dependencies for Aspect Sentiment Triplet Extraction,” IEEE/ACM Trans. Audio Speech Lang. Process., vol. 33, pp. 2527 – 2538, 2025.\n[2] X. Luo, J. Wang(*), and X. Zhang, “Utterance Alignment of Language Models for Effective User Simulation in Task-Oriented Dialogues”, IEEE/ACM Trans. Audio Speech Lang. Process., vol. 33, pp. 483 - 493, 2025.\n[3] J. Kong, J. Wang(*), and X. Zhang, “Adaptive Ensemble Self-Distillation with Consistent Gradients for Fast Inference of Pretrained Language Models,” IEEE/ACM Trans. Audio Speech Lang. Process., vol. 32, pp. 430–442, 2024.\n[4] J. Kong, J. Wang(*), L. C. Yu, and X. Zhang, “Multimodality Self-distillation for Fast Inference of Vision and Language Pretrained Models,” IEEE Trans. Multimed., vol. 26, pp. 8928–8940, 2024.\n[5] L. Yuan, J. Wang(*), L. C. Yu, and X. Zhang, “Encoding Syntactic Information into Transformers for Aspect-Based Sentiment Triplet Extraction,” IEEE Trans. Affect. Comput., no. 8, pp. 1–15, 2023.\n[6] Y. Zhang, J. Wang(*), L. C. Yu, D. Xu and X. Zhang, “Attribute-based injection transformer for personalized sentiment analysis, ” IEEE Transactions on Emerging Topics in Computational Intelligence, 2024.\n[7] L. Yuan, J. Wang(*), L. H. Lee, and X. Zhang, “Syntax-Enhanced Pretrained Language Models for Aspect-Level Sentiment Classification,” IEEE Trans. Comput. Soc. Syst., pp. 1–12, 2024.\n[8] T. Shen, E. Cambria, J. Wang(*), Y. Cai and X. Zhang, “Insight at the right spot: Provide decisive subgraph information to Graph LLM with reinforcement learning”, Information Fusion, 117, 102860.\n[9] L. Yuan, J. Wang(*), L. C. Yu and X. Zhang, “Hierarchical template transformer for fine-grained sentiment controllable generation”, Information Processing & Management, 59(5): 103048.\n[10] J. Wang, L. C. Yu, K. R. Lai and X. Zhang, “Community-based weighted graph model for valence-arousal prediction of affective words”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 24(11): 1957-1968.\n科研项目\n1. 国家自然科学基金NSFC地区项目“基于多维度连续情感空间的文本生成技术研究”，批准号：61966038，主持，经费40万，2020年1月至2023年12月。\n2. 国家自然科学基金NSFC青年基金项目“基于Valence-Arousal空间的维度型中文文本情感分析研究”，批准号：61702443，主持，经费29万，2018年1月至2020年12月。\n3. 第67 批次中国博士后基金面上项目“多维度文本情感理解与生成技术研究”，博士后编号：252064，批准号：2020M673220，主持，经费8 万。\n4. 云南省“千人计划”青年人才专项，项目资助经费50万，批准号：YNQR-QNRC-2018-018，2019年至2023年。\n5. 云南省基础研究项目面上项目“面向小语种的跨语文本情感分析技术研究”，经费10万，2020年7月 2022年6月。\n6. 教育部“产学合作、协同育人”项目“任务驱动的“机器学习”课程教学方法改革与网络教学模式的研究和实践”，百度公司合作，201802003007，2019年6月至2020年6月，3万元。\n7. 教育部“产学合作、协同育人”项目“面向虚拟化平台的人工智能课程改革研究和实践”，阿里云公司合作，202101001019，2021年07月至2023年06月，5万元。\n8. 云南省2023年本科教育教学改革研究项目“面向产业需求的智能科学与技术专业‘创新人才链’培养模式实践”，JG2023069，2023.9-2025.9，6万元。"