Collective Machine Learning for Semantic Data Interpretation

用于语义数据解释的集体机器学习

• 批准号: RGPIN-2017-06320
• 负责人: Guo, Yuhong
• 金额: $ 3.06万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=688441
• 关键词: Collective; Machine; Learning; Semantic; Data

This research will investigate collective machine learning algorithms that combine information from heterogeneous sources to automatically induce semantic interpreters of complex data. Two emerging trends motivate this research. First, in the era of big data there are an increasing number of freely available data sources that are relevant to any particular interpretation problem. Although such data sources vary in size and annotation coverage, their union can be leveraged to reduce the annotation cost required to achieve competence in a target interpretation task. Second, the growing success of machine learning has increased the ambition to move beyond learning simple classification models to adapting related semantic predictors across complex output categories.******The primary challenge of performing collective learning across complex output spaces lies in the heterogeneity of data sources, in terms of the different input features recorded, different output annotations captured, and different prediction tasks considered. To address this fundamental challenge, this research will develop novel representation learning algorithms that can uncover the shared structure underlying different data sets and different output targets.******If successful, this research program will overcome the boundaries of traditional machine learning and data analysis systems, and provide new tools for heterogeneous data analysis that address a significant need in the modern context of big data. Moreover, this research will also dramatically increase the autonomy and robustness of semantic data analysis systems while reducing, and in some cases eliminating, their dependence on human guidance.******The proposed research program has both fundamental and applied aspects and is expected to contribute progress in both respects. In particular, this research will not only contribute new mathematical and algorithmic developments in machine learning and data analysis research, it will also broaden the applicability of automated data analysis systems to a wider range of natural language processing, computer vision, bioinformatics, social and commercial data analysis problems. The resulting methods will be applicable to broad classes of heterogeneous data collected by governments, industry, organizations or individuals, and will significantly reduce the dependence on domain expertise in developing useful data interpretation systems.

这项研究将研究集体机器学习算法，该算法结合来自异构源的信息来自动诱导复杂数据的语义解释器。 两个新兴趋势推动了这项研究。 首先，在大数据时代，与任何特定解释问题相关的免费数据源的数量不断增加。 尽管此类数据源的大小和注释覆盖范围各不相同，但可以利用它们的联合来降低实现目标解释任务能力所需的注释成本。 其次，机器学习的日益成功增强了超越学习简单分类模型的野心，以适应复杂输出类别中的相关语义预测器。********跨复杂输出空间进行集体学习的主要挑战在于异质性数据源的不同，包括记录的不同输入特征、捕获的不同输出注释以及考虑的不同预测任务。 为了解决这一根本挑战，本研究将开发新颖的表示学习算法，该算法可以揭示不同数据集和不同输出目标背后的共享结构。******如果成功，该研究计划将克服传统机器学习和数据分析系统，并为异构数据分析提供新工具，满足现代大数据背景下的重大需求。 此外，这项研究还将极大地提高语义数据分析系统的自主性和鲁棒性，同时减少甚至在某些情况下消除它们对人类指导的依赖。******所提出的研究计划既有基础方面又有应用方面，并且是预计将在这两方面取得进展。 特别是，这项研究不仅将为机器学习和数据分析研究贡献新的数学和算法发展，还将把自动化数据分析系统的适用性拓宽到更广泛的自然语言处理、计算机视觉、生物信息学、社会和商业领域。数据分析问题。 由此产生的方法将适用于政府、行业、组织或个人收集的广泛类别的异构数据，并将显着减少开发有用的数据解释系统时对领域专业知识的依赖。