基於依存關係感知能力的深度學習模型進行金融推文之數值關係檢測

Numerical Relation Detection in Financial Tweets using Dependency-aware Deep Neural Network

近年來，許多研究是以金融文本資料進行分析，然而，我們發現在這些文本資料中的數字亦包含值得探討的豐富資訊。本論文藉由分析金融議題相關之推特文章，探討文本中目標數字與目標標籤是否具有關聯性。本研究採用基於變換器的雙向編碼器表示式作為模型架構之主要表示機制，並將依存關係作為特徵轉成依存關係矩陣後放入卷積神經網路中，使模型透過依存關係學習到文本中詞與詞間的關聯性。根據實驗結果顯示，本研究採用之方法對於此任務有良好之預測能力，其Macro-averging F1 Score為71.05%。

Machine learning methods for financial document analysis have been focusing mainly on the textual part. However, the numerical parts of these documents are also rich in information content. In order to further analyze the financial text, we should assay the numeric information in depth. In light of this, the purpose of this research is to identify the linking between the target cashtag and the target numeral in financial tweets, which is more challenging than analyzing news and official documents. In this research, we developed a multi model fusion approach which integrates Bidirectional Encoder Representations from Transformers (BERT) and Convolutional Neural Network (CNN). We also encode dependency information behind text into the model to derive semantic latent features. The experimental results show that our model can achieve remarkable performance and outperform comparisons.