过程支撑环境PSE(process supporting environment)是一种支持软件过程元过程的计算机环境,PSE通过运作一个事先定义好的软件过程模型SPM(software process model)来控制和指导实际软件开发过程.SPM使用的控制方式分为主动式(proactive...过程支撑环境PSE(process supporting environment)是一种支持软件过程元过程的计算机环境,PSE通过运作一个事先定义好的软件过程模型SPM(software process model)来控制和指导实际软件开发过程.SPM使用的控制方式分为主动式(proactive)和反应式(reactive)两种.由于主动式不能很好地支持软件过程的演化,反应式渐渐受到人们的重视.提出了一种反应式SPM以及建立这种模型所使用的图形化的软件过程建模语言,同时,对于所建立的SPM,提出用时序逻辑语言XYZ/E表示它的行为视图动态语义的方法.这为模型提供了明确的动态语义,为其运作和分析提供了形式化基础.展开更多
Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category...Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category detection as a flat classification problem. However, aspect categories are inter-related, and they are usually organized with a hierarchical tree structure. To leverage the structure information, this paper proposes a hierarchical multi-label classification model to detect aspect categories and uses a graph enhanced transformer network to integrate label dependency information into prediction features. Experiments have been conducted on four widely-used benchmark datasets, showing that the proposed model outperforms all strong baselines.展开更多
文摘过程支撑环境PSE(process supporting environment)是一种支持软件过程元过程的计算机环境,PSE通过运作一个事先定义好的软件过程模型SPM(software process model)来控制和指导实际软件开发过程.SPM使用的控制方式分为主动式(proactive)和反应式(reactive)两种.由于主动式不能很好地支持软件过程的演化,反应式渐渐受到人们的重视.提出了一种反应式SPM以及建立这种模型所使用的图形化的软件过程建模语言,同时,对于所建立的SPM,提出用时序逻辑语言XYZ/E表示它的行为视图动态语义的方法.这为模型提供了明确的动态语义,为其运作和分析提供了形式化基础.
基金supported by the National Natural Science Foundation of China under Grant No.62036001.
文摘Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category detection as a flat classification problem. However, aspect categories are inter-related, and they are usually organized with a hierarchical tree structure. To leverage the structure information, this paper proposes a hierarchical multi-label classification model to detect aspect categories and uses a graph enhanced transformer network to integrate label dependency information into prediction features. Experiments have been conducted on four widely-used benchmark datasets, showing that the proposed model outperforms all strong baselines.
