Finance-related news such as Bloomberg News, CNN Business and Forbes are
valuable sources of real data for market screening systems. In news, an expert
shares opinions beyond plain technical analyses that include context such as
political, sociological and cultural factors. In the same text, the expert
often discusses the performance of different assets. Some key statements are
mere descriptions of past events while others are predictions. Therefore,
understanding the temporality of the key statements in a text is essential to
separate context information from valuable predictions. We propose a novel
system to detect the temporality of finance-related news at discourse level
that combines Natural Language Processing and Machine Learning techniques, and
exploits sophisticated features such as syntactic and semantic dependencies.
More specifically, we seek to extract the dominant tenses of the main
statements, which may be either explicit or implicit. We have tested our system
on a labelled dataset of finance-related news annotated by researchers with
knowledge in the field. Experimental results reveal a high detection precision
compared to an alternative rule-based baseline approach. Ultimately, this
research contributes to the state-of-the-art of market screening by identifying
predictive knowledge for financial decision making.

通过自然语言处理和机器学习技术，我们提出一种能够在语篇层面检测金融相关新闻时态性的新系统，利用了句法和语义依赖等复杂特征，从而提取主要陈述的主导时态。实验结果表明，与一种基于规则的替代方法相比，该系统具有较高的检测精确度，最终有助于发掘金融决策的预测性知识。