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An approach to argumentation context mining from dialogue history in an e-market scenario

机译:电子市场情景中对话历史中对话历史上的论证中的一种方法

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In recent years, we have witnessed an increasing number of applications that combine AI and data mining to deliver sophisticated intelligent systems. While the two disciplines have been strongly correlated, either in research or application, there has never been a time where such level of convergence exists within a single system. >For example, building data models (e.g., clustering or frequent patterns) in high speed data streams require the use of machine learning techniques in AI to fix the problem of concept drifts and time-variations. In many scientific applications, where data is distributed and large, the concept of utility in AI is used to evaluate the cost of data mining tasks (e.g., data acquisition, data mining, and model utilization) so that knowledge discovery is practically feasible in resource constrained environments. Agent-based techniques are now used to reason and coordinate knowledge discovery tasks across distributed data repositories; neutral networks are now used to optimize data mining parameters; spectral clustering is now used in case-based reasoning (CBR) for medical discovery, and incremental learning is now the means to "idiot-proof" business intelligence systems. >Both disciplines have carried this potential to be used in a closed-loop fashion, where the reasoning of AI helps to "soften" the problems brought about by the brute force analytics of data mining. And data mining in turn, is the key to producing the relevant models and patterns that AI algorithms require. As users expect more from intelligent systems, there is further motivation for researchers of both disciplines to exploit the possibilities of what this closed-loop framework can potentially offer. >The objective of this workshop is to collect and report the experiences by researchers in either or both disciplines, and to offer an opportunity for researchers around the world to meet and share their ideas. The second run of this workshop received a total of 14 submissions of which, after a review by an international programme committee, we accepted 10 of them for presentation. We believe the articles in this collection will be of interest to many researchers either as a basis to build advanced intelligent systems, or as a platform to generate new ideas.
机译:近年来,我们目睹了越来越多的应用程序,即结合AI和数据挖掘来提供复杂的智能系统。虽然两所学科都在研究或应用中都强烈相关,但从来没有是一个系统内的这种收敛程度的时间。 >,例如,构建数据模型(例如,聚类或高速数据流中的频繁模式需要在AI中使用机器学习技术来解决概念漂移和时间变化的问题。在许多科学应用程序中,其中数据分布式并且AI中的实用程序概念用于评估数据挖掘任务的成本(例如,数据采集,数据挖掘和模型利用率),以便知识发现在资源中实际上是可行的受限环境。基于代理的技术现在用于跨分布式数据存储库的理解和协调知识发现任务;中性网络现在用于优化数据挖掘参数;现在使用光谱聚类,以便在基于案例的理解(CBR)中用于医学发现,并且增量学习现在是“IDIOT证明”商业智能系统的手段。 >这两个学科都携带了这种潜力以封闭的方式,AI的推理有助于“软化”数据挖掘的蛮力分析所带来的问题。和数据挖掘依次挖掘,是生产AI算法所需的相关模型和模式的关键。随着用户预期更多来自智能系统的,对两个学科的研究人员都有进一步的动机来利用这种闭环框架可能提供的可能性的可能性。 >本研讨会的目标是收集和报告研究人员在任何一个或两个学科的经验,并为世界各地的研究人员提供了一个思想和分享他们的想法的机会。该研讨会的第二次经营总共收到了14份提交的,在国际计划委员会审查后,我们接受了其中10个介绍。我们认为,这一系列的文章将是许多研究人员的感兴趣,也是构建高级智能系统的基础,或者作为生成新想法的平台。

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