CREAT: Census Research Exploration and Analysis Tool

New Uses of Health and Pension Information

January 2002

Written by: Julia I. Lane

Working Paper Number:

tp-2002-03

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.
:
payroll, report, agency, information, insurance, retirement, workforce, irs, coverage, insurance employer, pension, uninsured, retiree, filing, insured, health insurance, coverage employer

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.
:
Internal Revenue Service, Bureau of Labor Statistics, Social Security Administration, Small Business Administration, Center for Economic Studies, Urban Institute, Current Population Survey, Medical Expenditure Panel Survey, Employer Identification Numbers, Survey of Income and Program Participation, Department of Labor, American Community Survey, Longitudinal Employer Household Dynamics, Census Bureau Business Register, Business Register

Similar Working Papers Similarity between working papers are determined by an unsupervised neural network model know as Doc2Vec.

Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the 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 document vectors are compared using cosine similarity/distance to determine the most similar working papers. Papers identified with 🔥 are in the top 20% of similarity.

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