论文标题
利用基于图的方法的文本表示丰富：股票市场技术分析案例研究
Text Representation Enrichment Utilizing Graph based Approaches: Stock Market Technical Analysis Case Study
论文作者
论文摘要
图神经网络（GNN）最近用于各种自然语言处理（NLP）任务。图表表示中编码语料库的功能的能力使GNN模型在各种任务（例如文档分类）中流行。这种模型的一个主要缺点是它们主要在同质图上工作，而表示文本数据集则需要几种导致异质模式的节点类型。在本文中，我们提出了一种由无监督的节点表示模型组成的转导杂种方法，然后是节点分类/边缘预测模型。所提出的模型能够处理异质图以产生统一的节点嵌入，然后将其用于节点分类或链接预测作为下游任务。提出的模型的开发是为了对股票市场技术分析报告进行分类，据我们所知，这是该领域中的第一项工作。使用构造的数据集将其带走的实验证明了模型嵌入提取和下游任务的能力。
Graph neural networks (GNNs) have been utilized for various natural language processing (NLP) tasks lately. The ability to encode corpus-wide features in graph representation made GNN models popular in various tasks such as document classification. One major shortcoming of such models is that they mainly work on homogeneous graphs, while representing text datasets as graphs requires several node types which leads to a heterogeneous schema. In this paper, we propose a transductive hybrid approach composed of an unsupervised node representation learning model followed by a node classification/edge prediction model. The proposed model is capable of processing heterogeneous graphs to produce unified node embeddings which are then utilized for node classification or link prediction as the downstream task. The proposed model is developed to classify stock market technical analysis reports, which to our knowledge is the first work in this domain. Experiments, which are carried away using a constructed dataset, demonstrate the ability of the model in embedding extraction and the downstream tasks.
