Creating Data from Unstructured Text with Context Rule Assisted Machine Learning (CRAML)

Authors

Stephen Meisenbacher, Technical University of MunichFollow
Peter NorlanderFollow

Document Type

Working Paper

Publication Date

Winter 12-19-2022

Publication Title

GLO Discussions Paper

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.

Comments

Author Posting. © 2022, GLO Network. It is posted here for personal use, not for redistribution. The definitive version was published in GLO Discussion Papers

https://glabor.org/platform/discussion-papers/

Recommended Citation

Meisenbacher, Stephen; Norlander, Peter (2022) : Creating Data from Unstructured Text with Context Rule Assisted Machine Learning (CRAML), GLO Discussion Paper, No. 1214, Global Labor Organization (GLO), Essen

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