标题： 新闻分析中的弱监督：经济政策不确定性应用 显示英文标题

标题： Weak Supervision in Analysis of News: Application to Economic Policy Uncertainty

摘要： 及时的数据分析对于经济决策的需要促使大多数经济学家和政策制定者寻找非传统的补充数据来源。 在这一背景下，文本数据正被用来丰富传统数据来源，因为它易于收集且数量庞大。 我们的工作重点是研究文本数据，特别是新闻稿件，在衡量经济政策不确定性（EPU）方面的潜力。 经济政策不确定性被定义为公众无法预测新政策和未来经济基本面下的决策结果。 量化EPU对政策制定者、经济学家和投资者非常重要，因为它会影响他们对未来经济基本面的预期，从而影响他们的政策、投资和储蓄决策。 以往使用新闻文章测量EPU的大部分工作要么是人工的，要么基于简单的关键词搜索。 我们的工作提出了一种基于机器学习的解决方案，涉及弱监督来分类与经济政策不确定性相关的新闻文章。 弱监督被证明是在没有或仅有少量训练集的低资源环境中应用机器学习模型的一种高效方法，它利用领域知识和启发式方法。 我们进一步生成了一个基于弱监督的EPU指数，并将其与爱尔兰宏观经济指标一起用于进行广泛的计量经济学分析，以验证我们生成的指数是否预示着较弱的宏观经济表现。 显示英文摘要

摘要： The need for timely data analysis for economic decisions has prompted most economists and policy makers to search for non-traditional supplementary sources of data. In that context, text data is being explored to enrich traditional data sources because it is easy to collect and highly abundant. Our work focuses on studying the potential of textual data, in particular news pieces, for measuring economic policy uncertainty (EPU). Economic policy uncertainty is defined as the public's inability to predict the outcomes of their decisions under new policies and future economic fundamentals. Quantifying EPU is of great importance to policy makers, economists, and investors since it influences their expectations about the future economic fundamentals with an impact on their policy, investment and saving decisions. Most of the previous work using news articles for measuring EPU are either manual or based on a simple keyword search. Our work proposes a machine learning based solution involving weak supervision to classify news articles with regards to economic policy uncertainty. Weak supervision is shown to be an efficient machine learning paradigm for applying machine learning models in low resource settings with no or scarce training sets, leveraging domain knowledge and heuristics. We further generated a weak supervision based EPU index that we used to conduct extensive econometric analysis along with the Irish macroeconomic indicators to validate whether our generated index foreshadows weaker macroeconomic performance