Bank Crises and Investor Confidence
January 2009
Working Paper Number:
CES-09-02
Abstract
Document Tags and Keywords
Keywords
Keywords are automatically generated using KeyBERT, a powerful and innovative
keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant
keywords.
By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the
text, highlighting the most significant topics and trends. This approach not only enhances searchability but
provides connections that go beyond potentially domain-specific author-defined keywords.
:
investment,
econometric,
market,
financial,
finance,
investor,
immigrant,
bank,
borrower,
invest,
immigration,
saving,
risk,
migrant,
banking,
creditor,
immigrated
Tags
Tags are automatically generated using a pretrained language model from spaCy, which excels at
several tasks, including entity tagging.
The model is able to label words and phrases by part-of-speech,
including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are
identified to contain references to specific institutions, datasets, and other organizations.
:
Federal Reserve Bank,
Federal Reserve System,
Federal Reserve Bank of Chicago,
Chicago Census Research Data Center,
Survey of Income and Program Participation,
Department of Homeland Security,
Russell Sage Foundation,
Special Sworn Status
Similar Working Papers
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capture of semantic meaning in a way that relates to the context of words within the document. The model learns to
associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as
document classification, clustering, and similarity detection by preserving the order and structure of words. The
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