一种无结构金融公告多元关系抽取方法

Multiple Relationship Extraction from Unstructured Financial Announcements

金融公告信息披露了企业运营的关键数据,具有应用价值。无结构金融公告中涉及复杂的财务关系,即多元关系。该文设计了基于依存分析树和频繁子图挖掘的垂直域多元关系抽取方法TextMining,可大大降低对数据集的依赖。进一步,受图卷积神经网络启发,该文设计了垂直域优化的FTA-GCN算法。在构建的适用金融公告数据集上,算法较强地关注以金融公告中常见的名词实体为核心的多元关系抽取,实验结果表明,算法具有良好的抽取效果。

The financial announcement information discloses the key data of the company’s operation, involving complex financial relationships, namely multiple relationships. This paper designs TextMining, a vertical domain multivariate relationship extraction method based on dependency tree and frequent subgraph mining. Furthermore, inspired by the graph convolutional neural network, the FTA-GCN algorithm for vertical domain optimization is designed. In financial announcement dataset constructed in this paper, the algorithm can capture the multiple relationships between the common entities. Indicating that the algorithm has a good extraction performance.