Yi Yang

Joined

October 2023

Names

Yi Yang (Preferred)

Emails

****@iscas.ac.cn (Confirmed)

Personal Links

DBLP

ORCID

Career & Education History

Assistant Professor

Institute of Software, Institute of Software, Chinese Academy of Sciences (iscas.ac.cn)

2015 – 2025

Advisors, Relations & Conflicts

No relations added

Expertise

No areas of expertise listed

Publications

RulePrompt: Weakly Supervised Text Classification with Prompting PLMs and Self-Iterative Logical Rules
Miaomiao Li, Jiaqi Zhu, Yang Wang, Yi Yang, Yilin Li, Hongan Wang
- WWW 2024
- Readers: Everyone
LLM-Empowered Few-Shot Node Classification on Incomplete Graphs with Real Node Degrees
Yun Li, Yi Yang, Jiaqi Zhu, Hui Chen, Hongan Wang
- CIKM 2024
- Readers: Everyone
RulePrompt: Weakly Supervised Text Classification with Prompting PLMs and Self-Iterative Logical Rules
Miaomiao Li, Jiaqi Zhu, Yang Wang, Yi Yang, Yilin Li, Hongan Wang
- TheWebConf24 Oral
- Readers: Everyone
THGNN: An Embedding-based Model for Anomaly Detection in Dynamic Heterogeneous Social Networks
Yilin Li, Jiaqi Zhu, Congcong Zhang, Yi Yang, Jiawen Zhang, Ying Qiao, Hongan Wang
- CIKM 2023
- Readers: Everyone
CL-WSTC: Continual Learning for Weakly Supervised Text Classification on the Internet
Miaomiao Li, Jiaqi Zhu, Xin Yang, Yi Yang, Qiang Gao, Hongan Wang
- Published: 01 Jan 2023, Last Modified: 16 May 2023
- WWW 2023
- Readers: Everyone
Effective Seed-Guided Topic Labeling for Dataless Hierarchical Short Text Classification
Yi Yang, Hongan Wang, Jiaqi Zhu, Wandong Shi, Wenli Guo, Jiawen Zhang
- 2021 (modified: 18 Jan 2023)
- ICWE 2021
- Readers: Everyone
Knowledge-Enhanced Domain Adaptation in Few-Shot Relation Classification
Jiawen Zhang, Jiaqi Zhu, Yi Yang, Wandong Shi, Congcong Zhang, Hongan Wang
- 2021 (modified: 18 Jan 2023)
- KDD 2021
- Readers: Everyone
Dataless Short Text Classification Based on Biterm Topic Model and Word Embeddings
Yi Yang, Hongan Wang, Jiaqi Zhu, Yunkun Wu, Kailong Jiang, Wenli Guo, Wandong Shi
- 2020 (modified: 18 Jan 2023)
- IJCAI 2020
- Readers: Everyone