CREAT: Census Research Exploration and Analysis Tool

New Approaches to Confidentiality Protection Synthetic Data, Remote Access and Research Data Centers

June 2004

Working Paper Number:

tp-2004-03

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.
:
analysis, data, researcher, statistical, microdata, statistical agencies, study, respondent, technology, technological, social, research, information, statistician, federal, datasets, taxpayer

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.
:
Characteristics of Business Owners, Internal Revenue Service, Social Security Administration, Center for Economic Studies, Current Population Survey, Decennial Census, Survey of Income and Program Participation, Cornell University, Social Security, Research Data Center, Alfred P Sloan Foundation, LEHD Program, Master Beneficiary Record, Summary Earnings Records, Special Sworn Status

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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