Financial news items are unstructured sources of information that can be mined to extract knowledge for market screening applications. Manual extraction of relevant information from the continuous stream of finance-related news is cumbersome and beyond the skills of many investors, who, at most, can follow a few sources and authors. Accordingly, we focus on the analysis of financial news to identify relevant text and, within that text, forecasts and predictions. We propose a novel Natural Language Processing (NLP) system to assist investors in the detection of relevant financial events in unstructured textual sources by considering both relevance and temporality at the discursive level. Firstly, we segment the text to group together closely related text. Secondly, we apply co-reference resolution to discover internal dependencies within segments. Finally, we perform relevant topic modelling with Latent Dirichlet Allocation (LDA) to separate relevant from less relevant text and then analyse the relevant text using a Machine Learning-oriented temporal approach to identify predictions and speculative statements. We created an experimental data set composed of 2,158 financial news items that were manually labelled by NLP researchers to evaluate our solution. The ROUGE-L values for the identification of relevant text and predictions/forecasts were 0.662 and 0.982, respectively. To our knowledge, this is the first work to jointly consider relevance and temporality at the discursive level. It contributes to the transfer of human associative discourse capabilities to expert systems through the combination of multi-paragraph topic segmentation and co-reference resolution to separate author expression patterns, topic modelling with LDA to detect relevant text, and discursive temporality analysis to identify forecasts and predictions within this text.

翻译：金融新闻是非结构化信息来源，可通过数据挖掘提取知识以应用于市场筛选。投资者难以从持续不断的财经新闻流中手动提取相关信息，多数人最多只能追踪少量来源和作者。为此，我们聚焦于金融新闻分析以识别相关文本及其中的预测与预报内容。本文提出一种新型自然语言处理（NLP）系统，通过综合考虑语篇层面的相关性与时间性，辅助投资者检测非结构化文本源中的相关金融事件。首先，我们对文本进行分段以聚合紧密相关的文本单元；其次，应用共指消解技术发现段内文本的隐含依赖关系；最终，通过带潜在狄利克雷分配（LDA）的相关主题建模区分相关与非相关文本，并采用基于机器学习的时序分析方法识别预测性与推测性陈述。我们构建了包含2,158条金融新闻的实验数据集，并由NLP研究人员进行人工标注以评估方案性能。相关文本识别与预测/预报识别的ROUGE-L值分别为0.662和0.982。据我们所知，这是首项在语篇层面联合考虑相关性与时间性的研究。通过融合多段落主题分割与共指消解以分离作者表达模式、基于LDA的主题建模检测相关文本、以及语篇时序分析识别其中预测与预报，本研究为将人类联想式语篇能力迁移至专家系统提供了新路径。