知识抽取,即从不同来源、不同结构的数据中进行知识提取,形成知识(结构化数据)存入到知识图谱。

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报告主题: Fine-grained Opinion Mining: Current Trend and Cutting-Edge Dimensions

简介:

细粒度意见挖掘(也称为基于方面的情绪分析)旨在提取关于意见目标(方面)、意见持有者以及对他们表达的意见/情绪的知识,从而生成结构化的意见摘要。这项任务被证明是更重要和更有挑战性的,提供了一个深入的分析固执己见的文本,但在社区讨论不足,相比于整体情绪评分分类。本教程旨在回顾该领域现有的工作,包括3个主要的子任务,即基于方面的情感分类、与方面相关的提取和总结。我们介绍了各种模型结构,包括基于特征的、基于规则的和基于深度学习的模型,这些模型侧重于开发输入文本之间复杂的字级交互,并促进了这些方法的通用性,以用于有效的知识提取。除了单领域的研究,下一步是探索跨领域、跨语言和多模式的策略。尽管更具挑战性,但这些替代方案促进了细粒度意见挖掘的开发,因为在实际行业中,只有有限的资源可以使用细粒度注释。我们介绍了一些现有的研究,旨在为这些前沿的研究方向提供更多的见解。

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虞剑飞是新加坡管理大学信息系统学院研究员,他的研究集中在深度学习和迁移学习的许多自然语言处理任务,包括情绪分析、信息提取和问题回答。

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Multi-tasking optimization can usually achieve better performance than traditional single-tasking optimization through knowledge transfer between tasks. However, current multi-tasking optimization algorithms have some deficiencies. For high similarity problems, the knowledge that can accelerate the convergence rate of tasks has not been utilized fully. For low similarity problems, the probability of generating negative transfer is high, which may result in optimization performance degradation. In addition, some knowledge transfer methods proposed previously do not fully consider how to deal with the situation in which the population falls into local optimum. To solve these issues, a two stage adaptive knowledge transfer evolutionary multi-tasking optimization algorithm based on population distribution, labeled as EMT-PD, is proposed. EMT-PD can accelerate and improve the convergence performance of tasks based on the knowledge extracted from the probability model that reflects the search trend of the whole population. At the first transfer stage, an adaptive weight is used to adjust the step size of individual's search, which can reduce the impact of negative transfer. At the second stage of knowledge transfer, the individual's search range is further adjusted dynamically, which can increase the diversity of population and beneficial for jumping out of local optimum. Experimental results on multi-tasking multi-objective optimization test suites show that EMT-PD is superior to six state-of-the-art optimization algorithms. In order to further investigate the effectiveness of EMT-PD on many-objective optimization problems, a multi-tasking many-objective test suite is designed. The experimental results on it also demonstrate that EMT-PD has obvious competitiveness.

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