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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ahol-dl4nlp8/
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课程名称：深度学习在自然语言处理中的应用 - 第8部分 课程概述：本课程重点介绍图表示学习，特别是图神经网络（GNN）在图计算任务中的重要性，涵盖节点聚焦和图聚焦任务。GNN的革命性进展大大促进了图表示学习在现实应用中的广泛应用，尤其在推荐系统和社交网络分析等经典应用领域，GNN实现了最先进的性能，并推动了新的发展前沿。此外，GNN的新应用领域不断涌现，如组合优化、物理学和医疗保健等。这些广泛的应用使得GNN能够跨多个学科贡献新视角，成为一个真正跨学科的研究领域。 在课程中，我将首先介绍基本的图数据表示和概念，包括节点数据、边类型、邻接矩阵和拉普拉斯矩阵等。接下来，我们将讨论各种图学习任务，并探讨GNN所需的基本操作：过滤和池化。我们将详细讨论不同类型的图过滤方法，包括图卷积网络、图注意力网络、自信GCN、句法GCN和通用信息传递神经网络框架。然后，我们将讨论三种主要的图池化方法：基于拓扑的池化、全局池化和分层池化。在每种池化方法中，我们将讨论流行的方法，例如，在拓扑池化中主要讨论标准化切割和Graclus；在全局池化中讨论Set2Set和SortPool；在分层池化中讨论diffPool、gPool和SAGPool。 最后，我们将介绍三种无监督的图神经网络架构：GraphSAGE、图自编码器和深度图信息最大化。课程最后部分将探讨GNN在自然语言处理中的一些应用，包括语义角色标注、事件检测、多事件提取、神经机器翻译、文档时间标记和关系提取等。
More and more evidence has demonstrated that graph representation learning especially graph neural networks (GNNs) has tremendously facilitated computational tasks on graphs including both node-focused and graph-focused tasks. The revolutionary advances brought by GNNs have also immensely contributed to the depth and breadth of the adoption of graph representation learning in real-world applications. For the classical application domains of graph representation learning such as recommender systems and social network analysis, GNNs result in state-of-the-art performance and bring them into new frontiers. Meanwhile, new application domains of GNNs have been continuously emerging such as combinational optimization, physics, and healthcare. These wide applications of GNNs enable diverse contributions and perspectives from disparate disciplines and make this research field truly interdisciplinary.In this course, I will start by talking about basic graph data representation and concepts like node data, edge types, adjacency matrix and Laplacian matrix etc. Next, we will talk about broad kinds of graph learning tasks and discuss basic operations needed in a GNN: filtering and pooling. Further, we will discuss details of different types of graph filtering (i.e., neighborhood aggregation) methods. These include graph convolutional networks, graph attention networks, confidence GCNs, Syntactic GCNs and the general message passing neural network framework. Next, we will talk about three main types of graph pooling methods: Topology based pooling, Global pooling and Hierarchical pooling. Within each of these three types of graph pooling methods, we will discuss popular methods. For example, in topology pooling we will talk about Normalized Cut and Graclus mainly. In Global pooling, we will talk about Set2Set and SortPool. In Hierarchical pooling, we will talk about diffPool, gPool and SAGPool. Next, we will talk about three unsupervised graph neural network architectures: GraphSAGE, Graph auto-encoders and Deep Graph InfoMax. Lastly, we will talk about some applications of GNNs for NLP including semantic role labeling, event detection, multiple event extraction, neural machine translation, document timestamping and relation extraction.
