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摘要翻译：
《经济观察者调查》是日本政府发布的一项市场调查，包含了各领域人士对当前和未来经济状况的评估。虽然本调查为政策制定者提供了关于经济政策的见解，但没有对未来经济条件下的“未来”一词作出明确定义。因此，受访者在调查中提供的评估只是基于他们对“未来”含义的解释。这促使我们通过弱监督学习和文本挖掘来揭示他们在判断未来经济状况时对未来的不同解释。在我们的研究中，我们使用从正数据和未标记数据中学习（PU学习）将对未来经济状况的评估分为近未来和远未来的经济状况。由于数据集包含了多个时间段的数据，我们设计了一种新的结构，使神经网络能够基于多任务学习的思想进行PU学习，从而有效地学习分类器。实证分析表明，该方法可以对未来的经济状况进行分类，并对分类结果进行了解释，为决策提供了直观的依据。
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英文标题：
《Identifying Different Definitions of Future in the Assessment of Future
Economic Conditions: Application of PU Learning and Text Mining》
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作者：
Masahiro Kato
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最新提交年份：
2020
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
The Economy Watcher Survey, which is a market survey published by the Japanese government, contains \emph{assessments of current and future economic conditions} by people from various fields. Although this survey provides insights regarding economic policy for policymakers, a clear definition of the word "future" in future economic conditions is not provided. Hence, the assessments respondents provide in the survey are simply based on their interpretations of the meaning of "future." This motivated us to reveal the different interpretations of the future in their judgments of future economic conditions by applying weakly supervised learning and text mining. In our research, we separate the assessments of future economic conditions into economic conditions of the near and distant future using learning from positive and unlabeled data (PU learning). Because the dataset includes data from several periods, we devised new architecture to enable neural networks to conduct PU learning based on the idea of multi-task learning to efficiently learn a classifier. Our empirical analysis confirmed that the proposed method could separate the future economic conditions, and we interpreted the classification results to obtain intuitions for policymaking.
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PDF链接：
https://arxiv.org/pdf/1909.03348
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