A new GLO Discussion Paper demonstrates that CRAML produces document-level datasets for quantitative research and makes qualitative classification schemes scalable over large volumes of text.
GLO Discussion Paper No. 1214, 2022
Creating Data from Unstructured Text with Context Rule Assisted Machine Learning (CRAML) – Download PDF
by Meisenbacher, Stephen & Norlander, Peter
GLO Fellow Peter Norlander
Author Abstract: Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that builds structured, labeled datasets which are accurate and reproducible. CRAML enables domain experts to access uncommon constructs within a document corpus in a low-resource, transparent, and flexible manner. CRAML produces document-level datasets for quantitative research and makes qualitative classification schemes scalable over large volumes of text. We demonstrate that the method is useful for bibliographic analysis, transparent analysis of proprietary data, and expert classification of any documents with any scheme. To demonstrate this process for building data from text with Machine Learning, we publish open-source resources: the software, a new public document corpus, and a replicable analysis to build an interpretable classifier of suspected “no poach” clauses in franchise documents.
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PUBLISHED
Vol. 36, Issue 1, January 2023: Journal of Population Economics (JOPE) 16 articles. https://link.springer.com/journal/148/volumes-and-issues/36-1
Watch the videos of article presentations on December 1, 2022 during the GLO Global Conference 2022.
JOPE has CiteScore 6.5 (2021, LINK) & Impact Factor 4.7 (2021, LINK)
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