no code implementations • 2 Mar 2024 • Li Cai, Xin Mao, Zhihong Wang, Shangqing Zhao, Yuhao Zhou, Changxu Wu, Man Lan
Temporal knowledge graph completion (TKGC) aims to fill in missing facts within a given temporal knowledge graph at a specific time.
Knowledge Graph Completion Temporal Knowledge Graph Completion
1 code implementation • 9 Dec 2023 • Ziyi Ye, Xiaohui Xie, Qingyao Ai, Yiqun Liu, Zhihong Wang, Weihang Su, Min Zhang
To explore the effectiveness of brain signals in the context of RF, we propose a novel RF framework that combines BCI-based relevance feedback with pseudo-relevance signals and implicit signals to improve the performance of document re-ranking.
no code implementations • 19 Jul 2023 • Along He, Kai Wang, Zhihong Wang, Tao Li, Huazhu Fu
Firstly, the frozen features are transformed by an lightweight bottleneck layer to learn the domain-specific distribution of downstream medical tasks, and then a few learnable visual prompts are used as dynamic queries and then conduct cross-attention with the transformed features, attempting to acquire sample-specific knowledge that are suitable for each sample.
1 code implementation • 17 Aug 2022 • Ziyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang, Xuesong Chen, Min Zhang, Shaoping Ma
We explore the effectiveness of BTA for satisfaction modeling in two popular information access scenarios, i. e., search and recommendation.
1 code implementation • 6 Apr 2022 • Zhumin Chu, Qingyao Ai, Zhihong Wang, Yiqun Liu, Yingye Huang, Rui Zhang, Min Zhang, Shaoping Ma
This raises the question of how to accurately model user satisfaction in conversational search scenarios.
no code implementations • 22 Sep 2021 • Ziyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang, Xuancheng Li, Jiaji Li, Xuesong Chen, Min Zhang, Shaoping Ma
Inspired by these findings, we conduct supervised learning tasks to estimate the usefulness of non-click results with brain signals and conventional information (i. e., content and context factors).
1 code implementation • 3 Aug 2021 • Ziyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang, Xuesong Chen, Min Zhang, Shaoping Ma
In this paper, we carefully design a lab-based user study to investigate brain activities during reading comprehension.