no code implementations • CCL 2022 • Renwei Wu, Lin Li, Zheng He, Jingling Yuan
“当前, 基于方面类别的情感分析研究旨在将方面类别检测和面向类别的情感分类两个任务协同进行。然而, 现有研究未能有效关注情感数据集中存在的噪声标签, 影响了情感分析的质量。基于此, 本文提出一种标签先验知识增强的方面类别情感分析方法(AP-LPK)。首先本文为面向类别的情感分类构建了自回归提示训练方式, 可以激发预训练语言模型的潜力。同时该方式通过自回归生成标签词, 以期获得比非自回归更好的语义一致性。其次, 每个类别的标签分布作为标签先验知识引入, 并通过伯努利分布对其进行进一步精炼, 以用于减轻噪声标签的干扰。然后, AP-LPK将上述两个步骤分别得到的情感类别分布进行融合, 以获得最终的情感类别预测概率。最后, 本文提出的AP-LPK方法在五个数据集上进行评估, 包括SemEval 2015和2016的四个基准数据集和AI Challenger 2018的餐厅领域大规模数据集。实验结果表明, 本文提出的方法在F1指标上优于现有方法。”
no code implementations • 17 Feb 2024 • Xiaohua Wu, Lin Li, Xiaohui Tao, Frank Xing, Jingling Yuan
We achieve this through: (1) proving that multiple prediction models with additive factor attributions will have the desirable property of primary and secondary relations consistency, and (2) showing that factor relations with quantity can be represented as an importance distribution for encoding domain knowledge.
1 code implementation • 10 Aug 2023 • Huilin Zhu, Jingling Yuan, Xian Zhong, Zhengwei Yang, Zheng Wang, Shengfeng He
Domain adaptation is commonly employed in crowd counting to bridge the domain gaps between different datasets.
1 code implementation • 24 May 2022 • Ming Li, Lin Li, Qing Xie, Jingling Yuan, Xiaohui Tao
A publicly available dataset specialising in meal recommendation research for the research community is in urgent demand.
no code implementations • 3 Jul 2019 • Donghang Pan, Jingling Yuan, Lin Li, Deming Sheng
Based on the Bi-LSTM model, the classification model of word-level attention mechanism is studied.
no code implementations • 4 Oct 2018 • Chengyao Du, Jingling Yuan, Jiansheng Dong, Lin Li, Mincheng Chen, Tao Li
In order to solve these problems, we propose a real-time panoramic video stitching framework. The framework we propose mainly consists of three algorithms, LORB image feature extraction algorithm, feature point matching algorithm based on LSH and GPU parallel video stitching algorithm based on CUDA. The experiment results show that the algorithm mentioned can improve the performance in the stages of feature extraction of images stitching and matching, the running speed of which is 11 times than that of the traditional ORB algorithm and 639 times than that of the traditional SIFT algorithm.