no code implementations • COLING 2022 • Qing Yin, Zhihua Wang, Yunya Song, Yida Xu, Shuai Niu, Liang Bai, Yike Guo, Xian Yang
In this paper, we propose a novel DEC model, which we named the deep embedded clustering model with cluster-level representation learning (DECCRL) to jointly learn cluster and instance level representations.
no code implementations • 6 Nov 2022 • Yupeng Li, Haorui He, Shaonan Wang, Francis C. M. Lau, Yunya Song
In response, we address a new task called conversational stance detection which is to infer the stance towards a given target (e. g., COVID-19 vaccination) when given a data instance and its corresponding conversation thread.
1 code implementation • 18 Jan 2022 • Shuai Niu, Yunya Song, Qing Yin, Yike Guo, Xian Yang
Thirdly, both label-dependent and event-guided representations are integrated to make a robust prediction, in which the interpretability is enabled by the attention weights over words from medical notes.
1 code implementation • 18 Jan 2022 • Shuai Niu, Qing Yin, Yunya Song, Yike Guo, Xian Yang
In this paper, we propose a label dependent attention model LDAM to 1) improve the interpretability by exploiting Clinical-BERT (a biomedical language model pre-trained on a large clinical corpus) to encode biomedically meaningful features and labels jointly; 2) extend the idea of joint embedding to the processing of time-series data, and develop a multi-modal learning framework for integrating heterogeneous information from medical notes and time-series health status indicators.
no code implementations • 11 Jan 2016 • Guang-Neng Hu, Xin-yu Dai, Yunya Song, Shu-Jian Huang, Jia-Jun Chen
Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized choices.