Social media like Twitter who serves as a novel news medium and has become increasingly popular since its establishment. Large scale first-hand user-generated tweets motivate automatic event detection on Twitter. Prev...Social media like Twitter who serves as a novel news medium and has become increasingly popular since its establishment. Large scale first-hand user-generated tweets motivate automatic event detection on Twitter. Previous unsupervised approaches detected events by clustering words. These methods detect events using burstiness,which measures surging frequencies of words at certain time windows. However,event clusters represented by a set of individual words are difficult to understand. This issue is addressed by building a document-level event detection model that directly calculates the burstiness of tweets,leveraging distributed word representations for modeling semantic information,thereby avoiding sparsity. Results show that the document-level model not only offers event summaries that are directly human-readable,but also gives significantly improved accuracies compared to previous methods on unsupervised tweet event detection,which are based on words/segments.展开更多
RS10-CLOUD快速开发平台,是RS10-CLOUD云平台的重要组成部分,其隶属于国家重大项目,是一个面向零散制造业管理市场,支撑企业生产管理类实现的低代码开发平台。主要描述了基于RS10-CLOUD快速开发平台模块优化的过程。出发点在于面对当今...RS10-CLOUD快速开发平台,是RS10-CLOUD云平台的重要组成部分,其隶属于国家重大项目,是一个面向零散制造业管理市场,支撑企业生产管理类实现的低代码开发平台。主要描述了基于RS10-CLOUD快速开发平台模块优化的过程。出发点在于面对当今中国企业跨界转产已涵盖到了各个不同的领域的形势,转产过程中工业生产管理环境的业务数据分类及含义的二义性无形中增加了生产管理换件人工控制的成本。因此在RS10-CLOUD工业管理软件中,引入了工业标签统一管理动态生效的逻辑,其在开发阶段统一定义工业术语、工业业务标签并注入到页面;在软件应用阶段可以持续维护,并且实现页面自动生效。在这种开发模式的考量下,创新性地在此类软件中采用加入先验知识的神经网络机器翻译NMT进行训练,同时基于训练可行性和翻译模型的准确性,运用TLA(target language lemmas)进行约束训练,得到了从中文到英文的翻译模型。为汇总对比国内外工业属性的含义,最终实现多语言翻译,从而减少合资企业,输出型产业的生产管理成本提供考量。展开更多
基金Supported by the National High Technology Research and Development Programme of China(No.2015AA015405)
文摘Social media like Twitter who serves as a novel news medium and has become increasingly popular since its establishment. Large scale first-hand user-generated tweets motivate automatic event detection on Twitter. Previous unsupervised approaches detected events by clustering words. These methods detect events using burstiness,which measures surging frequencies of words at certain time windows. However,event clusters represented by a set of individual words are difficult to understand. This issue is addressed by building a document-level event detection model that directly calculates the burstiness of tweets,leveraging distributed word representations for modeling semantic information,thereby avoiding sparsity. Results show that the document-level model not only offers event summaries that are directly human-readable,but also gives significantly improved accuracies compared to previous methods on unsupervised tweet event detection,which are based on words/segments.
文摘RS10-CLOUD快速开发平台,是RS10-CLOUD云平台的重要组成部分,其隶属于国家重大项目,是一个面向零散制造业管理市场,支撑企业生产管理类实现的低代码开发平台。主要描述了基于RS10-CLOUD快速开发平台模块优化的过程。出发点在于面对当今中国企业跨界转产已涵盖到了各个不同的领域的形势,转产过程中工业生产管理环境的业务数据分类及含义的二义性无形中增加了生产管理换件人工控制的成本。因此在RS10-CLOUD工业管理软件中,引入了工业标签统一管理动态生效的逻辑,其在开发阶段统一定义工业术语、工业业务标签并注入到页面;在软件应用阶段可以持续维护,并且实现页面自动生效。在这种开发模式的考量下,创新性地在此类软件中采用加入先验知识的神经网络机器翻译NMT进行训练,同时基于训练可行性和翻译模型的准确性,运用TLA(target language lemmas)进行约束训练,得到了从中文到英文的翻译模型。为汇总对比国内外工业属性的含义,最终实现多语言翻译,从而减少合资企业,输出型产业的生产管理成本提供考量。
