Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/267553 
Erscheinungsjahr: 
2022
Schriftenreihe/Nr.: 
GLO Discussion Paper No. 1214
Verlag: 
Global Labor Organization (GLO), Essen
Zusammenfassung: 
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.
Schlagwörter: 
machine learning
natural language processing
text classification
big data
JEL: 
B41
C38
C81
C88
J08
J41
J42
J47
J53
Z13
Dokumentart: 
Working Paper

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