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Wasserstein Index Generation Model: Automatic Generation of Time-series Index with Application to Economic Policy Uncertainty
I propose a novel method, called the Wasserstein Index Generation model ...
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Qualitative Measures of Ambiguity
This paper introduces a qualitative measure of ambiguity and analyses it...
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Replication of the Keyword Extraction part of the paper "'Without the Clutter of Unimportant Words': Descriptive Keyphrases for Text Visualization"
"Keyword Extraction" refers to the task of automatically identifying the...
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Automatic Keyword Extraction for Text Summarization: A Survey
In recent times, data is growing rapidly in every domain such as news, s...
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Measuring and assessing economic uncertainty
This paper evaluates the dynamic response of economic activity to shocks...
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Keyword Spotter Model for Crop Pest and Disease Monitoring from Community Radio Data
In societies with well developed internet infrastructure, social media i...
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Uncertainty over Uncertainty: Investigating the Assumptions, Annotations, and Text Measurements of Economic Policy Uncertainty
Methods and applications are inextricably linked in science, and in particular in the domain of text-as-data. In this paper, we examine one such text-as-data application, an established economic index that measures economic policy uncertainty from keyword occurrences in news. This index, which is shown to correlate with firm investment, employment, and excess market returns, has had substantive impact in both the private sector and academia. Yet, as we revisit and extend the original authors' annotations and text measurements we find interesting text-as-data methodological research questions: (1) Are annotator disagreements a reflection of ambiguity in language? (2) Do alternative text measurements correlate with one another and with measures of external predictive validity? We find for this application (1) some annotator disagreements of economic policy uncertainty can be attributed to ambiguity in language, and (2) switching measurements from keyword-matching to supervised machine learning classifiers results in low correlation, a concerning implication for the validity of the index.
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