Arbeitspapier
Creating Data from Unstructured Text with Context Rule Assisted Machine Learning (CRAML)
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: GLO Discussion Paper ; No. 1214
- Klassifikation
-
Wirtschaft
Economic Methodology
Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
Data Collection and Data Estimation Methodology; Computer Programs: Other Computer Software
Labor Economics Policies
Labor Contracts
Monopsony; Segmented Labor Markets
Coercive Labor Markets
Labor-Management Relations; Industrial Jurisprudence
Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification
- Thema
-
machine learning
natural language processing
text classification
big data
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Meisenbacher, Stephen
Norlander, Peter
- Ereignis
-
Veröffentlichung
- (wer)
-
Global Labor Organization (GLO)
- (wo)
-
Essen
- (wann)
-
2022
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Meisenbacher, Stephen
- Norlander, Peter
- Global Labor Organization (GLO)
Entstanden
- 2022
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